





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 			__lowerCAmelCase				(					_UpperCamelCase			):


	'''simple docstring'''





	_A       	=							(DPMSolverMultistepScheduler,)
	_A       	=							(("num_inference_steps", 25),)

	def       snake_case__(	self:	Any,				**lowerCamelCase_:	List[Any] ):
			lowercase__				:			List[Any]            =		{
			    'num_train_timesteps': 1000,
			    'beta_start': 0.0_0_0_1,
			    'beta_end': 0.0_2,
			    'beta_schedule': 'linear',
			    'solver_order': 2,
			    'prediction_type': 'epsilon',
			    'thresholding': False,
			    'sample_max_value': 1.0,
			    'algorithm_type': 'dpmsolver++',
			    'solver_type': 'midpoint',
			    'lower_order_final': False,
			    'lambda_min_clipped': -float('inf' ),
			    'variance_type': None,
			}

			config.update(**lowerCamelCase_ )
			return config

	def       snake_case__(	self:	Tuple,				lowerCamelCase_:	Tuple=0,				**lowerCamelCase_:	List[Any] ):
			lowercase__				:			List[Any]            =		dict(self.forward_default_kwargs )
			lowercase__				:			Any            =		kwargs.pop('num_inference_steps',				lowerCamelCase_ )
			lowercase__				:			Optional[int]            =		self.dummy_sample
			lowercase__				:			Dict            =		0.1 * sample
			lowercase__				:			Optional[int]            =		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

					lowercase__      ,							lowercase__				:			Tuple            =		sample, sample
					for t in range(lowerCamelCase_,				time_step + scheduler.config.solver_order + 1 ):
							lowercase__				:			str            =		scheduler.step(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_,				**lowerCamelCase_ ).prev_sample
							lowercase__				:			Any            =		new_scheduler.step(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_,				**lowerCamelCase_ ).prev_sample

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

	def       snake_case__(	self:	Optional[int] ):
			pass

	def       snake_case__(	self:	List[str],				lowerCamelCase_:	Any=0,				**lowerCamelCase_:	str ):
			lowercase__				:			Optional[Any]            =		dict(self.forward_default_kwargs )
			lowercase__				:			Tuple            =		kwargs.pop('num_inference_steps',				lowerCamelCase_ )
			lowercase__				:			Optional[Any]            =		self.dummy_sample
			lowercase__				:			int            =		0.1 * sample
			lowercase__				:			Union[str, Any]            =		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

			for scheduler_class in self.scheduler_classes:
					lowercase__				:			Optional[Any]            =		self.get_scheduler_config()
					lowercase__				:			List[Any]            =		scheduler_class(**lowerCamelCase_ )
					scheduler.set_timesteps(lowerCamelCase_ )

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

					with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(lowerCamelCase_ )
							lowercase__				:			List[str]            =		scheduler_class.from_pretrained(lowerCamelCase_ )
							# copy over dummy past residuals
							new_scheduler.set_timesteps(lowerCamelCase_ )

							# copy over dummy past residual (must be after setting timesteps)
							lowercase__				:			List[str]            =		dummy_past_residuals[: new_scheduler.config.solver_order]

					lowercase__				:			str            =		scheduler.step(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_,				**lowerCamelCase_ ).prev_sample
					lowercase__				:			Optional[Any]            =		new_scheduler.step(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_,				**lowerCamelCase_ ).prev_sample

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

	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	int=None,				**lowerCamelCase_:	int ):
			if scheduler is None:
					lowercase__				:			str            =		self.scheduler_classes[0]
					lowercase__				:			int            =		self.get_scheduler_config(**lowerCamelCase_ )
					lowercase__				:			List[str]            =		scheduler_class(**lowerCamelCase_ )

			lowercase__				:			Optional[int]            =		10
			lowercase__				:			Tuple            =		self.dummy_model()
			lowercase__				:			List[str]            =		self.dummy_sample_deter
			scheduler.set_timesteps(lowerCamelCase_ )

			for i, t in enumerate(scheduler.timesteps ):
					lowercase__				:			Optional[Any]            =		model(lowerCamelCase_,				lowerCamelCase_ )
					lowercase__				:			Dict            =		scheduler.step(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_ ).prev_sample

			return sample

	def       snake_case__(	self:	str ):
			lowercase__				:			Dict            =		dict(self.forward_default_kwargs )

			lowercase__				:			List[Any]            =		kwargs.pop('num_inference_steps',				lowerCamelCase_ )

			for scheduler_class in self.scheduler_classes:
					lowercase__				:			str            =		self.get_scheduler_config()
					lowercase__				:			Any            =		scheduler_class(**lowerCamelCase_ )

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

					if num_inference_steps is not None and hasattr(lowerCamelCase_,				'set_timesteps' ):
							scheduler.set_timesteps(lowerCamelCase_ )
					elif num_inference_steps is not None and not hasattr(lowerCamelCase_,				'set_timesteps' ):
							lowercase__				:			List[Any]            =		num_inference_steps

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

					lowercase__				:			str            =		scheduler.timesteps[5]
					lowercase__				:			Union[str, Any]            =		scheduler.timesteps[6]

					lowercase__				:			Dict            =		scheduler.step(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_,				**lowerCamelCase_ ).prev_sample
					lowercase__				:			List[Any]            =		scheduler.step(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_,				**lowerCamelCase_ ).prev_sample

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

	def       snake_case__(	self:	Union[str, Any] ):
			for timesteps in [25, 50, 100, 999, 1000]:
					self.check_over_configs(num_train_timesteps=lowerCamelCase_ )

	def       snake_case__(	self:	Union[str, Any] ):
			self.check_over_configs(thresholding=lowerCamelCase_ )
			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=lowerCamelCase_,				prediction_type=lowerCamelCase_,				sample_max_value=lowerCamelCase_,				algorithm_type='dpmsolver++',				solver_order=lowerCamelCase_,				solver_type=lowerCamelCase_,				)

	def       snake_case__(	self:	Optional[int] ):
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=lowerCamelCase_ )

	def       snake_case__(	self:	Optional[Any] ):
			for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
					for solver_type in ["midpoint", "heun"]:
							for order in [1, 2, 3]:
									for prediction_type in ["epsilon", "sample"]:
											if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
													if order == 3:
															continue
											else:
													self.check_over_configs(
													    solver_order=lowerCamelCase_,				solver_type=lowerCamelCase_,				prediction_type=lowerCamelCase_,				algorithm_type=lowerCamelCase_,				)
											lowercase__				:			Optional[Any]            =		self.full_loop(
											    solver_order=lowerCamelCase_,				solver_type=lowerCamelCase_,				prediction_type=lowerCamelCase_,				algorithm_type=lowerCamelCase_,				)
											assert not torch.isnan(lowerCamelCase_ ).any(), "Samples have nan numbers"

	def       snake_case__(	self:	Union[str, Any] ):
			self.check_over_configs(lower_order_final=lowerCamelCase_ )
			self.check_over_configs(lower_order_final=lowerCamelCase_ )

	def       snake_case__(	self:	Optional[Any] ):
			self.check_over_configs(lambda_min_clipped=-float('inf' ) )
			self.check_over_configs(lambda_min_clipped=-5.1 )

	def       snake_case__(	self:	List[str] ):
			self.check_over_configs(variance_type=lowerCamelCase_ )
			self.check_over_configs(variance_type='learned_range' )

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

	def       snake_case__(	self:	int ):
			lowercase__				:			int            =		self.full_loop()
			lowercase__				:			List[Any]            =		torch.mean(torch.abs(lowerCamelCase_ ) )

			assert abs(result_mean.item() - 0.3_3_0_1 ) < 1E-3

	def       snake_case__(	self:	Optional[int] ):
			lowercase__				:			int            =		self.full_loop(thresholding=lowerCamelCase_,				dynamic_thresholding_ratio=0.8_7,				sample_max_value=0.5 )
			lowercase__				:			Optional[Any]            =		torch.mean(torch.abs(lowerCamelCase_ ) )

			assert abs(result_mean.item() - 1.1_3_6_4 ) < 1E-3

	def       snake_case__(	self:	List[Any] ):
			lowercase__				:			List[str]            =		self.full_loop(prediction_type='v_prediction' )
			lowercase__				:			str            =		torch.mean(torch.abs(lowerCamelCase_ ) )

			assert abs(result_mean.item() - 0.2_2_5_1 ) < 1E-3

	def       snake_case__(	self:	Dict ):
			lowercase__				:			str            =		self.full_loop(prediction_type='v_prediction',				use_karras_sigmas=lowerCamelCase_ )
			lowercase__				:			str            =		torch.mean(torch.abs(lowerCamelCase_ ) )

			assert abs(result_mean.item() - 0.2_0_9_6 ) < 1E-3

	def       snake_case__(	self:	Any ):
			# make sure that iterating over schedulers with same config names gives same results
			# for defaults
			lowercase__				:			Tuple            =		DPMSolverMultistepScheduler(**self.get_scheduler_config() )
			lowercase__				:			Tuple            =		self.full_loop(scheduler=lowerCamelCase_ )
			lowercase__				:			str            =		torch.mean(torch.abs(lowerCamelCase_ ) )

			assert abs(result_mean.item() - 0.3_3_0_1 ) < 1E-3

			lowercase__				:			str            =		DPMSolverSinglestepScheduler.from_config(scheduler.config )
			lowercase__				:			Dict            =		UniPCMultistepScheduler.from_config(scheduler.config )
			lowercase__				:			int            =		DEISMultistepScheduler.from_config(scheduler.config )
			lowercase__				:			List[str]            =		DPMSolverMultistepScheduler.from_config(scheduler.config )

			lowercase__				:			Tuple            =		self.full_loop(scheduler=lowerCamelCase_ )
			lowercase__				:			Union[str, Any]            =		torch.mean(torch.abs(lowerCamelCase_ ) )

			assert abs(result_mean.item() - 0.3_3_0_1 ) < 1E-3

	def       snake_case__(	self:	Union[str, Any] ):
			lowercase__				:			List[Any]            =		self.scheduler_classes[0]
			lowercase__				:			Union[str, Any]            =		self.get_scheduler_config(thresholding=lowerCamelCase_,				dynamic_thresholding_ratio=0 )
			lowercase__				:			Tuple            =		scheduler_class(**lowerCamelCase_ )

			lowercase__				:			int            =		10
			lowercase__				:			int            =		self.dummy_model()
			lowercase__				:			Optional[int]            =		self.dummy_sample_deter.half()
			scheduler.set_timesteps(lowerCamelCase_ )

			for i, t in enumerate(scheduler.timesteps ):
					lowercase__				:			Tuple            =		model(lowerCamelCase_,				lowerCamelCase_ )
					lowercase__				:			int            =		scheduler.step(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_ ).prev_sample

			assert sample.dtype == torch.floataa





	def       snake_case__(	self:	List[str],				**lowerCamelCase_:	Union[str, Any] ):
			for scheduler_class in self.scheduler_classes:
					lowercase__				:			str            =		self.get_scheduler_config(**lowerCamelCase_ )
					lowercase__				:			List[Any]            =		scheduler_class(**lowerCamelCase_ )

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









def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   int )      ->       bool:




		'''simple docstring'''
		lowercase__				:			Optional[int]            =		9

		while num > 0:
				if num % 10 != digit:
						return False
				num //= 100
				digit -= 1

		return True


def 			SCREAMING_SNAKE_CASE__     (					)      ->       int:




		'''simple docstring'''
		lowercase__				:			List[str]            =		138_902_663

		while not is_square_form(num * num ):
				if num % 10 == 3:
						num -= 6  # (3 - 6) % 10 = 7
				else:
						num -= 4  # (7 - 4) % 10 = 3

		return num * 10


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


