



'''simple docstring'''
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     (DEISMultistepScheduler,)
	lowercase__    							=     (("""num_inference_steps""", 25),)





	def       UpperCamelCase_    (					self   :							Any					,**lowerCamelCase__   :							Tuple					):







							'''simple docstring'''
							_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,
							}

							config.update(**lowerCamelCase__					)
							return config





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							Optional[int]=0					,**lowerCamelCase__   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  dict(self.forward_default_kwargs					)
							_UpperCamelCase   :						List[str]												=  kwargs.pop('num_inference_steps'					,lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  self.dummy_sample
							_UpperCamelCase   :						List[str]												=  0.1 * sample
							_UpperCamelCase   :						Optional[Any]												=  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

													_UpperCamelCase							,     _UpperCamelCase   :						Any												=  sample, sample
													for t in range(lowerCamelCase__					,time_step + scheduler.config.solver_order + 1					):
																			_UpperCamelCase   :						int												=  scheduler.step(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__					).prev_sample
																			_UpperCamelCase   :						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       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							pass





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							List[Any]=0					,**lowerCamelCase__   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  dict(self.forward_default_kwargs					)
							_UpperCamelCase   :						int												=  kwargs.pop('num_inference_steps'					,lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  self.dummy_sample
							_UpperCamelCase   :						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   :						Optional[Any]												=  self.get_scheduler_config()
													_UpperCamelCase   :						Any												=  scheduler_class(**lowerCamelCase__					)
													scheduler.set_timesteps(lowerCamelCase__					)

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(lowerCamelCase__					)
																			_UpperCamelCase   :						List[Any]												=  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)
																			_UpperCamelCase   :						Union[str, Any]												=  dummy_past_residuals[: new_scheduler.config.solver_order]

													_UpperCamelCase   :						Any												=  scheduler.step(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__					).prev_sample
													_UpperCamelCase   :						str												=  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       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							str=None					,**lowerCamelCase__   :							List[str]					):







							'''simple docstring'''
							if scheduler is None:
													_UpperCamelCase   :						Union[str, Any]												=  self.scheduler_classes[0]
													_UpperCamelCase   :						List[str]												=  self.get_scheduler_config(**lowerCamelCase__					)
													_UpperCamelCase   :						Any												=  scheduler_class(**lowerCamelCase__					)

							_UpperCamelCase   :						int												=  self.scheduler_classes[0]
							_UpperCamelCase   :						Optional[int]												=  self.get_scheduler_config(**lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  scheduler_class(**lowerCamelCase__					)

							_UpperCamelCase   :						List[str]												=  10
							_UpperCamelCase   :						List[str]												=  self.dummy_model()
							_UpperCamelCase   :						Union[str, Any]												=  self.dummy_sample_deter
							scheduler.set_timesteps(lowerCamelCase__					)

							for i, t in enumerate(scheduler.timesteps					):
													_UpperCamelCase   :						str												=  model(lowerCamelCase__					,lowerCamelCase__					)
													_UpperCamelCase   :						int												=  scheduler.step(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					).prev_sample

							return sample





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  dict(self.forward_default_kwargs					)

							_UpperCamelCase   :						int												=  kwargs.pop('num_inference_steps'					,lowerCamelCase__					)

							for scheduler_class in self.scheduler_classes:
													_UpperCamelCase   :						List[str]												=  self.get_scheduler_config()
													_UpperCamelCase   :						List[Any]												=  scheduler_class(**lowerCamelCase__					)

													_UpperCamelCase   :						Optional[Any]												=  self.dummy_sample
													_UpperCamelCase   :						List[Any]												=  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'					):
																			_UpperCamelCase   :						str												=  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   :						str												=  dummy_past_residuals[: scheduler.config.solver_order]

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

													_UpperCamelCase   :						List[str]												=  scheduler.step(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__					).prev_sample
													_UpperCamelCase   :						int												=  scheduler.step(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__					).prev_sample

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





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							_UpperCamelCase   :						Optional[Any]												=  DEISMultistepScheduler(**self.get_scheduler_config()					)
							_UpperCamelCase   :						List[Any]												=  self.full_loop(scheduler=lowerCamelCase__					)
							_UpperCamelCase   :						Union[str, Any]												=  torch.mean(torch.abs(lowerCamelCase__					)					)

							assert abs(result_mean.item() - 0.2_3_9_1_6					) < 1E-3

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

							_UpperCamelCase   :						Any												=  self.full_loop(scheduler=lowerCamelCase__					)
							_UpperCamelCase   :						Union[str, Any]												=  torch.mean(torch.abs(lowerCamelCase__					)					)

							assert abs(result_mean.item() - 0.2_3_9_1_6					) < 1E-3





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							self.check_over_configs(thresholding=lowerCamelCase__					)
							for order in [1, 2, 3]:
													for solver_type in ["logrho"]:
																			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='deis'					,solver_order=lowerCamelCase__					,solver_type=lowerCamelCase__					,)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							for algorithm_type in ["deis"]:
													for solver_type in ["logrho"]:
																			for order in [1, 2, 3]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    solver_order=lowerCamelCase__					,solver_type=lowerCamelCase__					,prediction_type=lowerCamelCase__					,algorithm_type=lowerCamelCase__					,)
																															_UpperCamelCase   :						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       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							self.check_over_configs(lower_order_final=lowerCamelCase__					)
							self.check_over_configs(lower_order_final=lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							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       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  self.full_loop()
							_UpperCamelCase   :						Tuple												=  torch.mean(torch.abs(lowerCamelCase__					)					)

							assert abs(result_mean.item() - 0.2_3_9_1_6					) < 1E-3





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  self.full_loop(prediction_type='v_prediction'					)
							_UpperCamelCase   :						Any												=  torch.mean(torch.abs(lowerCamelCase__					)					)

							assert abs(result_mean.item() - 0.0_9_1					) < 1E-3





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  self.scheduler_classes[0]
							_UpperCamelCase   :						str												=  self.get_scheduler_config(thresholding=lowerCamelCase__					,dynamic_thresholding_ratio=0					)
							_UpperCamelCase   :						List[str]												=  scheduler_class(**lowerCamelCase__					)

							_UpperCamelCase   :						str												=  10
							_UpperCamelCase   :						Any												=  self.dummy_model()
							_UpperCamelCase   :						List[str]												=  self.dummy_sample_deter.half()
							scheduler.set_timesteps(lowerCamelCase__					)

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

							assert sample.dtype == torch.floataa







'''simple docstring'''
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						return base * power(UpperCAmelCase_    ,    (exponent - 1)				) if exponent else 1


if __name__ == "__main__":
					print('Raise base to the power of exponent using recursion...')
					snake_case_					:      int    			=    int(input('Enter the base: ').strip())
					snake_case_					:      Optional[int]    			=    int(input('Enter the exponent: ').strip())
					snake_case_					:      Optional[int]    			=    power(base, abs(exponent))
					if exponent < 0:  # power() does not properly deal w/ negative exponents
										snake_case_					:      List[Any]    			=    1 / result
					print(F"""{base} to the power of {exponent} is {result}""")


