



"""simple docstring"""







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     (DPMSolverSinglestepScheduler,)
			__magic_name__     					=     (("""num_inference_steps""", 25),)



			def 				__lowercase					(			self							:				List[Any]   ,	**_lowercase							:				Optional[Any]		)							->					Union[str, Any]:
									snake_case   :			Dict						    = {
									    "num_train_timesteps": 1000,
									    "beta_start": 0.0001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									    "solver_order": 2,
									    "prediction_type": "epsilon",
									    "thresholding": False,
									    "sample_max_value": 1.0,
									    "algorithm_type": "dpmsolver++",
									    "solver_type": "midpoint",
									    "lambda_min_clipped": -float("inf"		),
									    "variance_type": None,
									}

									config.update(**_lowercase		)
									return config



			def 				__lowercase					(			self							:				Dict   ,	_lowercase							:				int=0   ,	**_lowercase							:				int		)							->					str:
									snake_case   :			Dict						    = dict(self.forward_default_kwargs		)
									snake_case   :			Optional[Any]						    = kwargs.pop("num_inference_steps"   ,	_lowercase		)
									snake_case   :			Tuple						    = self.dummy_sample
									snake_case   :			Union[str, Any]						    = 0.1 * sample
									snake_case   :			Union[str, Any]						    = [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
															snake_case   :			List[Any]						    = self.get_scheduler_config(**_lowercase		)
															snake_case   :			Optional[Any]						    = scheduler_class(**_lowercase		)
															scheduler.set_timesteps(_lowercase		)
															# copy over dummy past residuals
															snake_case   :			Any						    = dummy_past_residuals[: scheduler.config.solver_order]

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

															snake_case    ,			snake_case   :			Optional[int]						    = sample, sample
															for t in range(_lowercase   ,	time_step + scheduler.config.solver_order + 1		):
																					snake_case   :			str						    = scheduler.step(_lowercase   ,	_lowercase   ,	_lowercase   ,	**_lowercase		).prev_sample
																					snake_case   :			Any						    = 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 				__lowercase					(			self							:				Optional[Any]		)							->					Dict:
									pass



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				Optional[Any]=0   ,	**_lowercase							:				List[Any]		)							->					Optional[int]:
									snake_case   :			List[str]						    = dict(self.forward_default_kwargs		)
									snake_case   :			int						    = kwargs.pop("num_inference_steps"   ,	_lowercase		)
									snake_case   :			int						    = self.dummy_sample
									snake_case   :			Optional[int]						    = 0.1 * sample
									snake_case   :			Dict						    = [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
															snake_case   :			Optional[int]						    = self.get_scheduler_config()
															snake_case   :			Union[str, Any]						    = scheduler_class(**_lowercase		)
															scheduler.set_timesteps(_lowercase		)

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

															with tempfile.TemporaryDirectory() as tmpdirname:
																					scheduler.save_config(_lowercase		)
																					snake_case   :			int						    = 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)
																					snake_case   :			int						    = dummy_past_residuals[: new_scheduler.config.solver_order]

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

									snake_case   :			Optional[int]						    = self.scheduler_classes[0]
									snake_case   :			Union[str, Any]						    = self.get_scheduler_config(**_lowercase		)
									snake_case   :			Any						    = scheduler_class(**_lowercase		)

									snake_case   :			List[Any]						    = 10
									snake_case   :			List[Any]						    = self.dummy_model()
									snake_case   :			List[str]						    = self.dummy_sample_deter
									scheduler.set_timesteps(_lowercase		)

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

									return sample



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					List[Any]:
									snake_case   :			Optional[Any]						    = DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
									snake_case   :			Optional[int]						    = 50
									snake_case   :			Optional[int]						    = self.dummy_model()
									snake_case   :			Any						    = self.dummy_sample_deter
									scheduler.set_timesteps(_lowercase		)

									# make sure that the first t is uneven
									for i, t in enumerate(scheduler.timesteps[3:]		):
															snake_case   :			List[str]						    = model(_lowercase   ,	_lowercase		)
															snake_case   :			int						    = scheduler.step(_lowercase   ,	_lowercase   ,	_lowercase		).prev_sample

									snake_case   :			Optional[Any]						    = torch.mean(torch.abs(_lowercase		)		)

									assert abs(result_mean.item() - 0.2574		) < 1E-3



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



			def 				__lowercase					(			self							:				Any		)							->					Tuple:
									# make sure that iterating over schedulers with same config names gives same results
									# for defaults
									snake_case   :			Union[str, Any]						    = DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
									snake_case   :			Union[str, Any]						    = self.full_loop(scheduler=_lowercase		)
									snake_case   :			int						    = torch.mean(torch.abs(_lowercase		)		)

									assert abs(result_mean.item() - 0.2791		) < 1E-3

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

									snake_case   :			Union[str, Any]						    = self.full_loop(scheduler=_lowercase		)
									snake_case   :			int						    = torch.mean(torch.abs(_lowercase		)		)

									assert abs(result_mean.item() - 0.2791		) < 1E-3



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Tuple:
									self.check_over_configs(thresholding=_lowercase		)
									for order in [1, 2, 3]:
															for solver_type in ["midpoint", "heun"]:
																					for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																	self.check_over_configs(
																																	    thresholding=_lowercase   ,	prediction_type=_lowercase   ,	sample_max_value=_lowercase   ,	algorithm_type="dpmsolver++"   ,	solver_order=_lowercase   ,	solver_type=_lowercase   ,	)



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



			def 				__lowercase					(			self							:				Dict		)							->					Optional[Any]:
									for algorithm_type in ["dpmsolver", "dpmsolver++"]:
															for solver_type in ["midpoint", "heun"]:
																					for order in [1, 2, 3]:
																											for prediction_type in ["epsilon", "sample"]:
																																	self.check_over_configs(
																																	    solver_order=_lowercase   ,	solver_type=_lowercase   ,	prediction_type=_lowercase   ,	algorithm_type=_lowercase   ,	)
																																	snake_case   :			Dict						    = self.full_loop(
																																	    solver_order=_lowercase   ,	solver_type=_lowercase   ,	prediction_type=_lowercase   ,	algorithm_type=_lowercase   ,	)
																																	assert not torch.isnan(_lowercase		).any(), "Samples have nan numbers"



			def 				__lowercase					(			self							:				str		)							->					List[str]:
									self.check_over_configs(lower_order_final=_lowercase		)
									self.check_over_configs(lower_order_final=_lowercase		)



			def 				__lowercase					(			self							:				int		)							->					List[Any]:
									self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
									self.check_over_configs(lambda_min_clipped=-5.1		)



			def 				__lowercase					(			self							:				Dict		)							->					List[Any]:
									self.check_over_configs(variance_type=_lowercase		)
									self.check_over_configs(variance_type="learned_range"		)



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



			def 				__lowercase					(			self							:				Any		)							->					List[Any]:
									snake_case   :			str						    = self.full_loop()
									snake_case   :			Optional[Any]						    = torch.mean(torch.abs(_lowercase		)		)

									assert abs(result_mean.item() - 0.2791		) < 1E-3



			def 				__lowercase					(			self							:				Optional[Any]		)							->					List[str]:
									snake_case   :			int						    = self.full_loop(use_karras_sigmas=_lowercase		)
									snake_case   :			Any						    = torch.mean(torch.abs(_lowercase		)		)

									assert abs(result_mean.item() - 0.2248		) < 1E-3



			def 				__lowercase					(			self							:				int		)							->					Tuple:
									snake_case   :			int						    = self.full_loop(prediction_type="v_prediction"		)
									snake_case   :			Any						    = torch.mean(torch.abs(_lowercase		)		)

									assert abs(result_mean.item() - 0.1453		) < 1E-3



			def 				__lowercase					(			self							:				Optional[int]		)							->					str:
									snake_case   :			Tuple						    = self.full_loop(prediction_type="v_prediction"   ,	use_karras_sigmas=_lowercase		)
									snake_case   :			List[str]						    = torch.mean(torch.abs(_lowercase		)		)

									assert abs(result_mean.item() - 0.0649		) < 1E-3



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

									snake_case   :			int						    = 10
									snake_case   :			Union[str, Any]						    = self.dummy_model()
									snake_case   :			Tuple						    = self.dummy_sample_deter.half()
									scheduler.set_timesteps(_lowercase		)

									for i, t in enumerate(scheduler.timesteps		):
															snake_case   :			Any						    = model(_lowercase   ,	_lowercase		)
															snake_case   :			int						    = scheduler.step(_lowercase   ,	_lowercase   ,	_lowercase		).prev_sample

									assert sample.dtype == torch.floataa








"""simple docstring"""







import math
import sys

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						if number != int(lowerCamelCase_ ):
												raise ValueError("the value of input must be a natural number" )
						if number < 0:
												raise ValueError("the value of input must not be a negative number" )
						if number == 0:
												return 1
						snake_case   :			List[str]						    = [-1] * (number + 1)
						snake_case   :			str						    = 0
						for i in range(1     ,		number + 1 ):
												snake_case   :			Tuple						    = sys.maxsize
												snake_case   :			Optional[Any]						    = int(math.sqrt(lowerCamelCase_ ) )
												for j in range(1     ,		root + 1 ):
																		snake_case   :			List[str]						    = 1 + answers[i - (j**2)]
																		snake_case   :			List[str]						    = min(lowerCamelCase_     ,		lowerCamelCase_ )
												snake_case   :			Dict						    = answer
						return answers[number]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



