
'''simple docstring'''







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     (IPNDMScheduler,)
					lowerCAmelCase_        =     (('''num_inference_steps''', 5_0),)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       {'''num_train_timesteps''': 10_00}
											config.update(**__SCREAMING_SNAKE_CASE		)
											return config






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE=0				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     str													=       dict(self.forward_default_kwargs		)
											lowercase_	:     List[Any]													=       kwargs.pop('''num_inference_steps'''				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       self.dummy_sample
											lowercase_	:     Optional[Any]													=       0.1 * sample
											lowercase_	:     List[Any]													=       [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(**__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     int													=       scheduler_class(**__SCREAMING_SNAKE_CASE		)
																	scheduler.set_timesteps(__SCREAMING_SNAKE_CASE		)
																	# copy over dummy past residuals
																	lowercase_	:     Optional[Any]													=       dummy_past_residuals[:]

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

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

																	lowercase_	:     Optional[int]													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample
																	lowercase_	:     List[Any]													=       new_scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample

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

																	lowercase_	:     List[str]													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample
																	lowercase_	:     Dict													=       new_scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample

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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											pass






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE=0				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       dict(self.forward_default_kwargs		)
											lowercase_	:     Tuple													=       kwargs.pop('''num_inference_steps'''				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       self.dummy_sample
											lowercase_	:     Dict													=       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_	:     Optional[int]													=       self.get_scheduler_config()
																	lowercase_	:     Any													=       scheduler_class(**__SCREAMING_SNAKE_CASE		)
																	scheduler.set_timesteps(__SCREAMING_SNAKE_CASE		)

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

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

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

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

																	lowercase_	:     Optional[Any]													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample
																	lowercase_	:     Dict													=       new_scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample

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

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

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






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     str													=       self.scheduler_classes[0]
											lowercase_	:     Optional[Any]													=       self.get_scheduler_config(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Dict													=       scheduler_class(**__SCREAMING_SNAKE_CASE		)

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

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

											for i, t in enumerate(scheduler.timesteps		):
																	lowercase_	:     List[Any]													=       model(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     Union[str, Any]													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		).prev_sample

											return sample






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       dict(self.forward_default_kwargs		)

											lowercase_	:     List[str]													=       kwargs.pop('''num_inference_steps'''				,							__SCREAMING_SNAKE_CASE		)

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

																	lowercase_	:     Union[str, Any]													=       self.dummy_sample
																	lowercase_	:     str													=       0.1 * sample

																	if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE				,							'''set_timesteps'''		):
																							scheduler.set_timesteps(__SCREAMING_SNAKE_CASE		)
																	elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE				,							'''set_timesteps'''		):
																							lowercase_	:     Any													=       num_inference_steps

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

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

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

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

																	lowercase_	:     Optional[int]													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample
																	lowercase_	:     Tuple													=       scheduler.step(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		).prev_sample

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






					def 	_snake_case    (   self		):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       self.full_loop()
											lowercase_	:     Optional[Any]													=       torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

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





from functools import lru_cache



@lru_cache
def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					if num < 0:
										raise ValueError('''Number should not be negative.'''		)

					return 1 if num in (0, 1) else num * factorial(num - 1		)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




