



'''simple docstring'''






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 				SCREAMING_SNAKE_CASE(							lowerCAmelCase_			):







	"""simple docstring"""






	lowerCamelCase__			 =       (UniPCMultistepScheduler,)
	lowerCamelCase__			 =       (("""num_inference_steps""", 25),)







	def      A	(						self			:     List[Any]		,			**__snake_case			:     str		)			->		Any:
								UpperCAmelCase :       Any 							=		{
								    '''num_train_timesteps''': 1000,
								    '''beta_start''': 0.00_01,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''solver_order''': 2,
								    '''solver_type''': '''bh2''',
								}

								config.update(**__snake_case		)
								return config







	def      A	(						self			:     List[Any]		,			__snake_case			:     Union[str, Any]=0		,			**__snake_case			:     Any		)			->		List[Any]:
								UpperCAmelCase :       int 							=		dict(self.forward_default_kwargs		)
								UpperCAmelCase :       str 							=		kwargs.pop('''num_inference_steps'''		,			__snake_case		)
								UpperCAmelCase :       int 							=		self.dummy_sample
								UpperCAmelCase :       str 							=		0.1 * sample
								UpperCAmelCase :       List[str] 							=		[residual + 0.2, residual + 0.15, residual + 0.10]

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

															with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(__snake_case		)
																						UpperCAmelCase :       List[str] 							=		scheduler_class.from_pretrained(__snake_case		)
																						new_scheduler.set_timesteps(__snake_case		)
																						# copy over dummy past residuals
																						UpperCAmelCase :       Optional[int] 							=		dummy_past_residuals[: new_scheduler.config.solver_order]

															UpperCAmelCase :       Union[str, Any] 							=		sample, sample
															for t in range(__snake_case		,			time_step + scheduler.config.solver_order + 1		):
																						UpperCAmelCase :       int 							=		scheduler.step(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample
																						UpperCAmelCase :       Any 							=		new_scheduler.step(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample

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







	def      A	(						self			:     List[Any]		,			__snake_case			:     List[str]=0		,			**__snake_case			:     str		)			->		Optional[Any]:
								UpperCAmelCase :       Tuple 							=		dict(self.forward_default_kwargs		)
								UpperCAmelCase :       Optional[int] 							=		kwargs.pop('''num_inference_steps'''		,			__snake_case		)
								UpperCAmelCase :       List[Any] 							=		self.dummy_sample
								UpperCAmelCase :       List[str] 							=		0.1 * sample
								UpperCAmelCase :       List[str] 							=		[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase :       Tuple 							=		self.get_scheduler_config()
															UpperCAmelCase :       int 							=		scheduler_class(**__snake_case		)
															scheduler.set_timesteps(__snake_case		)

															# copy over dummy past residuals (must be after setting timesteps)
															UpperCAmelCase :       Optional[int] 							=		dummy_past_residuals[: scheduler.config.solver_order]

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

																						# copy over dummy past residual (must be after setting timesteps)
																						UpperCAmelCase :       Optional[int] 							=		dummy_past_residuals[: new_scheduler.config.solver_order]

															UpperCAmelCase :       Dict 							=		scheduler.step(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample
															UpperCAmelCase :       str 							=		new_scheduler.step(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample

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







	def      A	(						self			:     Any		,			__snake_case			:     Union[str, Any]=None		,			**__snake_case			:     List[Any]		)			->		Optional[int]:
								if scheduler is None:
															UpperCAmelCase :       Any 							=		self.scheduler_classes[0]
															UpperCAmelCase :       Dict 							=		self.get_scheduler_config(**__snake_case		)
															UpperCAmelCase :       List[str] 							=		scheduler_class(**__snake_case		)

								UpperCAmelCase :       int 							=		self.scheduler_classes[0]
								UpperCAmelCase :       Optional[Any] 							=		self.get_scheduler_config(**__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		scheduler_class(**__snake_case		)

								UpperCAmelCase :       List[str] 							=		10
								UpperCAmelCase :       Any 							=		self.dummy_model()
								UpperCAmelCase :       Optional[int] 							=		self.dummy_sample_deter
								scheduler.set_timesteps(__snake_case		)

								for i, t in enumerate(scheduler.timesteps		):
															UpperCAmelCase :       Optional[int] 							=		model(__snake_case		,			__snake_case		)
															UpperCAmelCase :       Union[str, Any] 							=		scheduler.step(__snake_case		,			__snake_case		,			__snake_case		).prev_sample

								return sample







	def      A	(						self			:     Union[str, Any]		)			->		str:
								UpperCAmelCase :       Optional[Any] 							=		dict(self.forward_default_kwargs		)

								UpperCAmelCase :       str 							=		kwargs.pop('''num_inference_steps'''		,			__snake_case		)

								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase :       Optional[int] 							=		self.get_scheduler_config()
															UpperCAmelCase :       Union[str, Any] 							=		scheduler_class(**__snake_case		)

															UpperCAmelCase :       List[Any] 							=		self.dummy_sample
															UpperCAmelCase :       str 							=		0.1 * sample

															if num_inference_steps is not None and hasattr(__snake_case		,			'''set_timesteps'''		):
																						scheduler.set_timesteps(__snake_case		)
															elif num_inference_steps is not None and not hasattr(__snake_case		,			'''set_timesteps'''		):
																						UpperCAmelCase :       Optional[int] 							=		num_inference_steps

															# copy over dummy past residuals (must be done after set_timesteps)
															UpperCAmelCase :       Tuple 							=		[residual + 0.2, residual + 0.15, residual + 0.10]
															UpperCAmelCase :       Any 							=		dummy_past_residuals[: scheduler.config.solver_order]

															UpperCAmelCase :       str 							=		scheduler.timesteps[5]
															UpperCAmelCase :       Any 							=		scheduler.timesteps[6]

															UpperCAmelCase :       Dict 							=		scheduler.step(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample
															UpperCAmelCase :       Dict 							=		scheduler.step(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample

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







	def      A	(						self			:     Union[str, Any]		)			->		Dict:
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								UpperCAmelCase :       Union[str, Any] 							=		UniPCMultistepScheduler(**self.get_scheduler_config()		)
								UpperCAmelCase :       List[str] 							=		self.full_loop(scheduler=__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_mean.item() - 0.24_64		) < 1E-3

								UpperCAmelCase :       int 							=		DPMSolverSinglestepScheduler.from_config(scheduler.config		)
								UpperCAmelCase :       Union[str, Any] 							=		DEISMultistepScheduler.from_config(scheduler.config		)
								UpperCAmelCase :       str 							=		DPMSolverMultistepScheduler.from_config(scheduler.config		)
								UpperCAmelCase :       Optional[int] 							=		UniPCMultistepScheduler.from_config(scheduler.config		)

								UpperCAmelCase :       Dict 							=		self.full_loop(scheduler=__snake_case		)
								UpperCAmelCase :       Any 							=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_mean.item() - 0.24_64		) < 1E-3







	def      A	(						self			:     Optional[Any]		)			->		Dict:
								for timesteps in [25, 50, 100, 999, 1000]:
															self.check_over_configs(num_train_timesteps=__snake_case		)







	def      A	(						self			:     int		)			->		Dict:
								self.check_over_configs(thresholding=__snake_case		)
								for order in [1, 2, 3]:
															for solver_type in ["bh1", "bh2"]:
																						for threshold in [0.5, 1.0, 2.0]:
																													for prediction_type in ["epsilon", "sample"]:
																																				self.check_over_configs(
																																				    thresholding=__snake_case		,			prediction_type=__snake_case		,			sample_max_value=__snake_case		,			solver_order=__snake_case		,			solver_type=__snake_case		,			)







	def      A	(						self			:     Optional[int]		)			->		Optional[Any]:
								for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=__snake_case		)







	def      A	(						self			:     Tuple		)			->		Optional[int]:
								for solver_type in ["bh1", "bh2"]:
															for order in [1, 2, 3]:
																						for prediction_type in ["epsilon", "sample"]:
																													self.check_over_configs(
																													    solver_order=__snake_case		,			solver_type=__snake_case		,			prediction_type=__snake_case		,			)
																													UpperCAmelCase :       List[str] 							=		self.full_loop(
																													    solver_order=__snake_case		,			solver_type=__snake_case		,			prediction_type=__snake_case		,			)
																													assert not torch.isnan(__snake_case		).any(), "Samples have nan numbers"







	def      A	(						self			:     Dict		)			->		str:
								self.check_over_configs(lower_order_final=__snake_case		)
								self.check_over_configs(lower_order_final=__snake_case		)







	def      A	(						self			:     int		)			->		List[str]:
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
															self.check_over_forward(num_inference_steps=__snake_case		,			time_step=0		)







	def      A	(						self			:     Union[str, Any]		)			->		Optional[Any]:
								UpperCAmelCase :       Optional[Any] 							=		self.full_loop()
								UpperCAmelCase :       str 							=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_mean.item() - 0.24_64		) < 1E-3







	def      A	(						self			:     List[str]		)			->		Any:
								UpperCAmelCase :       Optional[Any] 							=		self.full_loop(prediction_type='''v_prediction'''		)
								UpperCAmelCase :       Union[str, Any] 							=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_mean.item() - 0.10_14		) < 1E-3







	def      A	(						self			:     List[Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       str 							=		self.scheduler_classes[0]
								UpperCAmelCase :       List[Any] 							=		self.get_scheduler_config(thresholding=__snake_case		,			dynamic_thresholding_ratio=0		)
								UpperCAmelCase :       str 							=		scheduler_class(**__snake_case		)

								UpperCAmelCase :       Tuple 							=		10
								UpperCAmelCase :       List[Any] 							=		self.dummy_model()
								UpperCAmelCase :       List[Any] 							=		self.dummy_sample_deter.half()
								scheduler.set_timesteps(__snake_case		)

								for i, t in enumerate(scheduler.timesteps		):
															UpperCAmelCase :       List[str] 							=		model(__snake_case		,			__snake_case		)
															UpperCAmelCase :       Optional[int] 							=		scheduler.step(__snake_case		,			__snake_case		,			__snake_case		).prev_sample

								assert sample.dtype == torch.floataa







	def      A	(						self			:     Optional[int]		,			**__snake_case			:     Optional[int]		)			->		List[str]:
								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase :       Optional[Any] 							=		self.get_scheduler_config(**__snake_case		)
															UpperCAmelCase :       List[str] 							=		scheduler_class(**__snake_case		)

															scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
															assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps










"""simple docstring"""






from functools import lru_cache






def      snake_case__   (		__lowerCamelCase				:    int	):
			"""simple docstring"""
			lowerCamelCase__		:		Optional[Any]			=2
			lowerCamelCase__		:		Optional[int]			=set()
			while i * i <= n:
						if n % i:
									i += 1
						else:
									n //= i
									factors.add(__lowerCamelCase	)
			if n > 1:
						factors.add(__lowerCamelCase	)
			return factors






@lru_cache
def      snake_case__   (		__lowerCamelCase				:    int	):
			"""simple docstring"""
			return len(unique_prime_factors(__lowerCamelCase	)	)






def      snake_case__   (		__lowerCamelCase				:    list	):
			"""simple docstring"""
			return len(set(__lowerCamelCase	)	) in (0, 1)






def      snake_case__   (		__lowerCamelCase				:    int	):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple			=2

			while True:
						# Increment each value of a generated range
						lowerCamelCase__		:		Tuple			=[base + i for i in range(__lowerCamelCase	)]

						# Run elements through out unique_prime_factors function
						# Append our target number to the end.
						lowerCamelCase__		:		Optional[Any]			=[upf_len(__lowerCamelCase	) for x in group]
						checker.append(__lowerCamelCase	)

						# If all numbers in the list are equal, return the group variable.
						if equality(__lowerCamelCase	):
									return group

						# Increment our base variable by 1
						base += 1






def      snake_case__   (		__lowerCamelCase				:    int = 4	):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]			=run(__lowerCamelCase	)
			return results[0] if len(__lowerCamelCase	) else None


if __name__ == "__main__":
		print(solution())




