



"""simple docstring"""




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			List[Any]      =  (IPNDMScheduler,)
						UpperCamelCase__      :			Optional[Any]      =  (("num_inference_steps", 50),)






						def 	_A     (   self				,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= {'num_train_timesteps': 1_000}
													config.update(**_A		)
													return config






						def 	_A     (   self				,							_A=0				,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= dict(self.forward_default_kwargs		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('num_inference_steps'				,							_A		)
													__SCREAMING_SNAKE_CASE									= self.dummy_sample
													__SCREAMING_SNAKE_CASE									= 0.1 * sample
													__SCREAMING_SNAKE_CASE									= [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

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

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

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






						def 	_A     (   self		):







													'''simple docstring'''

													pass






						def 	_A     (   self				,							_A=0				,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= dict(self.forward_default_kwargs		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('num_inference_steps'				,							_A		)
													__SCREAMING_SNAKE_CASE									= self.dummy_sample
													__SCREAMING_SNAKE_CASE									= 0.1 * sample
													__SCREAMING_SNAKE_CASE									= [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

																				# 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(_A		)
																											__SCREAMING_SNAKE_CASE									= 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)
																											__SCREAMING_SNAKE_CASE									= dummy_past_residuals[:]

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

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

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

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






						def 	_A     (   self				,							**_A		):







													'''simple docstring'''

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

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

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

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

													return sample






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= dict(self.forward_default_kwargs		)

													__SCREAMING_SNAKE_CASE									= kwargs.pop('num_inference_steps'				,							_A		)

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

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

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

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

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

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

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

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






						def 	_A     (   self		):







													'''simple docstring'''

													for timesteps in [100, 1_000]:
																				self.check_over_configs(num_train_timesteps=_A				,							time_step=_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													for t, num_inference_steps in zip([1, 5, 10]				,							[10, 50, 100]		):
																				self.check_over_forward(num_inference_steps=_A				,							time_step=_A		)






						def 	_A     (   self		):







													'''simple docstring'''

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

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











def      __lowercase       (			a__ = 10_00       )    ->			int:
							__SCREAMING_SNAKE_CASE									= -1
							__SCREAMING_SNAKE_CASE									= 0
							for a in range(1							,       n // 3       ):
														# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
														__SCREAMING_SNAKE_CASE									= (n * n - 2 * a * n) // (2 * n - 2 * a)
														__SCREAMING_SNAKE_CASE									= n - a - b
														if c * c == (a * a + b * b):
																					__SCREAMING_SNAKE_CASE									= a * b * c
																					if candidate >= product:
																												__SCREAMING_SNAKE_CASE									= candidate
							return product


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






