



'''simple docstring'''




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				(UniPCMultistepScheduler,)
	lowercase    =				(('num_inference_steps', 25),)





	def 				_lowercase(						self	,	**A					)    ->    List[str]:
								UpperCAmelCase  :	List[str]							  =	{
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0_0_0_1,
								    """beta_end""": 0.0_2,
								    """beta_schedule""": """linear""",
								    """solver_order""": 2,
								    """solver_type""": """bh2""",
								}

								config.update(**A					)
								return config





	def 				_lowercase(						self	,	A=0	,	**A					)    ->    Optional[int]:
								UpperCAmelCase  :	Optional[Any]							  =	dict(self.forward_default_kwargs					)
								UpperCAmelCase  :	Optional[Any]							  =	kwargs.pop("""num_inference_steps"""	,	A					)
								UpperCAmelCase  :	Optional[int]							  =	self.dummy_sample
								UpperCAmelCase  :	Dict							  =	0.1 * sample
								UpperCAmelCase  :	int							  =	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase  :	str							  =	self.get_scheduler_config(**A					)
															UpperCAmelCase  :	Tuple							  =	scheduler_class(**A					)
															scheduler.set_timesteps(A					)
															# copy over dummy past residuals
															UpperCAmelCase  :	Tuple							  =	dummy_past_residuals[: scheduler.config.solver_order]

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

															UpperCAmelCase  ,  UpperCAmelCase  :	List[Any]							  =	sample, sample
															for t in range(A	,	time_step + scheduler.config.solver_order + 1					):
																						UpperCAmelCase  :	Any							  =	scheduler.step(A	,	A	,	A	,	**A					).prev_sample
																						UpperCAmelCase  :	str							  =	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 				_lowercase(						self	,	A=0	,	**A					)    ->    int:
								UpperCAmelCase  :	Dict							  =	dict(self.forward_default_kwargs					)
								UpperCAmelCase  :	Tuple							  =	kwargs.pop("""num_inference_steps"""	,	A					)
								UpperCAmelCase  :	Any							  =	self.dummy_sample
								UpperCAmelCase  :	Optional[int]							  =	0.1 * sample
								UpperCAmelCase  :	List[str]							  =	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase  :	List[str]							  =	self.get_scheduler_config()
															UpperCAmelCase  :	List[Any]							  =	scheduler_class(**A					)
															scheduler.set_timesteps(A					)

															# 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(A					)
																						UpperCAmelCase  :	Union[str, Any]							  =	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)
																						UpperCAmelCase  :	List[str]							  =	dummy_past_residuals[: new_scheduler.config.solver_order]

															UpperCAmelCase  :	Tuple							  =	scheduler.step(A	,	A	,	A	,	**A					).prev_sample
															UpperCAmelCase  :	int							  =	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 				_lowercase(						self	,	A=None	,	**A					)    ->    Any:
								if scheduler is None:
															UpperCAmelCase  :	Optional[int]							  =	self.scheduler_classes[0]
															UpperCAmelCase  :	List[str]							  =	self.get_scheduler_config(**A					)
															UpperCAmelCase  :	Optional[int]							  =	scheduler_class(**A					)

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

								UpperCAmelCase  :	int							  =	10
								UpperCAmelCase  :	Tuple							  =	self.dummy_model()
								UpperCAmelCase  :	List[str]							  =	self.dummy_sample_deter
								scheduler.set_timesteps(A					)

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

								return sample





	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	Optional[Any]							  =	dict(self.forward_default_kwargs					)

								UpperCAmelCase  :	Dict							  =	kwargs.pop("""num_inference_steps"""	,	A					)

								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase  :	List[Any]							  =	self.get_scheduler_config()
															UpperCAmelCase  :	Optional[Any]							  =	scheduler_class(**A					)

															UpperCAmelCase  :	List[Any]							  =	self.dummy_sample
															UpperCAmelCase  :	List[str]							  =	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"""					):
																						UpperCAmelCase  :	List[Any]							  =	num_inference_steps

															# copy over dummy past residuals (must be done after set_timesteps)
															UpperCAmelCase  :	Dict							  =	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
															UpperCAmelCase  :	Union[str, Any]							  =	dummy_past_residuals[: scheduler.config.solver_order]

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

															UpperCAmelCase  :	Optional[int]							  =	scheduler.step(A	,	A	,	A	,	**A					).prev_sample
															UpperCAmelCase  :	int							  =	scheduler.step(A	,	A	,	A	,	**A					).prev_sample

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





	def 				_lowercase(						self					)    ->    Any:
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								UpperCAmelCase  :	Tuple							  =	UniPCMultistepScheduler(**self.get_scheduler_config()					)
								UpperCAmelCase  :	Any							  =	self.full_loop(scheduler=A					)
								UpperCAmelCase  :	Tuple							  =	torch.mean(torch.abs(A					)					)

								assert abs(result_mean.item() - 0.2_4_6_4					) < 1e-3

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

								UpperCAmelCase  :	str							  =	self.full_loop(scheduler=A					)
								UpperCAmelCase  :	List[str]							  =	torch.mean(torch.abs(A					)					)

								assert abs(result_mean.item() - 0.2_4_6_4					) < 1e-3





	def 				_lowercase(						self					)    ->    str:
								for timesteps in [25, 50, 100, 999, 1000]:
															self.check_over_configs(num_train_timesteps=A					)





	def 				_lowercase(						self					)    ->    Optional[int]:
								self.check_over_configs(thresholding=A					)
								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=A	,	prediction_type=A	,	sample_max_value=A	,	solver_order=A	,	solver_type=A	,	)





	def 				_lowercase(						self					)    ->    Tuple:
								for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=A					)





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





	def 				_lowercase(						self					)    ->    Optional[int]:
								self.check_over_configs(lower_order_final=A					)
								self.check_over_configs(lower_order_final=A					)





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





	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	List[Any]							  =	self.full_loop()
								UpperCAmelCase  :	List[Any]							  =	torch.mean(torch.abs(A					)					)

								assert abs(result_mean.item() - 0.2_4_6_4					) < 1e-3





	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	List[Any]							  =	self.full_loop(prediction_type="""v_prediction"""					)
								UpperCAmelCase  :	int							  =	torch.mean(torch.abs(A					)					)

								assert abs(result_mean.item() - 0.1_0_1_4					) < 1e-3





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

								UpperCAmelCase  :	Dict							  =	10
								UpperCAmelCase  :	Union[str, Any]							  =	self.dummy_model()
								UpperCAmelCase  :	str							  =	self.dummy_sample_deter.half()
								scheduler.set_timesteps(A					)

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

								assert sample.dtype == torch.floataa






	def 				_lowercase(						self	,	**A					)    ->    Union[str, Any]:
								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase  :	List[str]							  =	self.get_scheduler_config(**A					)
															UpperCAmelCase  :	Tuple							  =	scheduler_class(**A					)

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






'''simple docstring'''




import math



def    __lowerCamelCase						(      _lowercase	)   ->    bool:
							assert isinstance(_lowercase			,       _lowercase	) and (
							    number >= 0
							), "'number' must been an int and positive"

							if 1 < number < 4:
														# 2 and 3 are primes
														return True
							elif number < 2 or not number % 2:
														# Negatives, 0, 1 and all even numbers are not primes
														return False

							UpperCAmelCase  :	str							  =	range(3			,       int(math.sqrt(_lowercase	) + 1	)			,       2	)
							return not any(not number % i for i in odd_numbers	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase=1			,       **_lowercase	)   ->    Union[str, Any]:
							UpperCAmelCase  :	Optional[int]							  =	factor * value
							UpperCAmelCase  :	List[Any]							  =	value

							while not is_prime(_lowercase	):
														value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

							if value == first_value_val:
														return next_prime(value + 1			,       **_lowercase	)
							return value

