


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      Any      =		(DPMSolverMultistepScheduler,)
				SCREAMING_SNAKE_CASE__    :      Dict      =		(('''num_inference_steps''', 25),)


				def 		__magic_name__(	self      :Tuple      ,      **lowerCAmelCase__      :Tuple     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Any		      =		{
										    '''num_train_timesteps''': 1_000,
										    '''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''',
										    '''lower_order_final''': False,
										    '''lambda_min_clipped''': -float('''inf'''     ),
										    '''variance_type''': None,
										}

										config.update(**lowerCAmelCase__     )
										return config


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

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

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

																__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					str		      =		sample, sample
																for t in range(lowerCAmelCase__      ,      time_step + scheduler.config.solver_order + 1     ):
																						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		scheduler.step(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      **lowerCAmelCase__     ).prev_sample
																						__SCREAMING_SNAKE_CASE						:					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 		__magic_name__(	self      :Optional[Any]     )		->      List[str]:
										pass


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

										for scheduler_class in self.scheduler_classes:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.get_scheduler_config()
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		scheduler_class(**lowerCAmelCase__     )
																scheduler.set_timesteps(lowerCAmelCase__     )

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

																with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(lowerCAmelCase__     )
																						__SCREAMING_SNAKE_CASE						:					Tuple		      =		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)
																						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		dummy_past_residuals[: new_scheduler.config.solver_order]

																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		scheduler.step(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      **lowerCAmelCase__     ).prev_sample
																__SCREAMING_SNAKE_CASE						:					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 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :Any=None      ,      **lowerCAmelCase__      :Dict     )		->      Optional[Any]:
										if scheduler is None:
																__SCREAMING_SNAKE_CASE						:					int		      =		self.scheduler_classes[0]
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.get_scheduler_config(**lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					str		      =		scheduler_class(**lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		10
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.dummy_model()
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.dummy_sample_deter
										scheduler.set_timesteps(lowerCAmelCase__     )

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

										return sample


				def 		__magic_name__(	self      :int     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		dict(self.forward_default_kwargs     )

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		kwargs.pop('''num_inference_steps'''      ,      lowerCAmelCase__     )

										for scheduler_class in self.scheduler_classes:
																__SCREAMING_SNAKE_CASE						:					Any		      =		self.get_scheduler_config()
																__SCREAMING_SNAKE_CASE						:					Any		      =		scheduler_class(**lowerCAmelCase__     )

																__SCREAMING_SNAKE_CASE						:					Dict		      =		self.dummy_sample
																__SCREAMING_SNAKE_CASE						:					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'''     ):
																						__SCREAMING_SNAKE_CASE						:					Any		      =		num_inference_steps

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

																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		scheduler.timesteps[5]
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		scheduler.timesteps[6]

																__SCREAMING_SNAKE_CASE						:					int		      =		scheduler.step(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      **lowerCAmelCase__     ).prev_sample
																__SCREAMING_SNAKE_CASE						:					Optional[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 		__magic_name__(	self      :List[str]     )		->      int:
										for timesteps in [25, 50, 100, 999, 1_000]:
																self.check_over_configs(num_train_timesteps=lowerCAmelCase__     )


				def 		__magic_name__(	self      :Any     )		->      int:
										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 		__magic_name__(	self      :List[str]     )		->      Optional[Any]:
										for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=lowerCAmelCase__     )


				def 		__magic_name__(	self      :List[str]     )		->      Optional[int]:
										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__      ,      )
																																		__SCREAMING_SNAKE_CASE						:					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 		__magic_name__(	self      :Optional[Any]     )		->      Dict:
										self.check_over_configs(lower_order_final=lowerCAmelCase__     )
										self.check_over_configs(lower_order_final=lowerCAmelCase__     )


				def 		__magic_name__(	self      :str     )		->      List[str]:
										self.check_over_configs(lambda_min_clipped=-float('''inf'''     )     )
										self.check_over_configs(lambda_min_clipped=-5.1     )


				def 		__magic_name__(	self      :str     )		->      List[Any]:
										self.check_over_configs(variance_type=lowerCAmelCase__     )
										self.check_over_configs(variance_type='''learned_range'''     )


				def 		__magic_name__(	self      :Tuple     )		->      Union[str, Any]:
										for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																self.check_over_forward(num_inference_steps=lowerCAmelCase__      ,      time_step=0     )


				def 		__magic_name__(	self      :List[Any]     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.full_loop()
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		torch.mean(torch.abs(lowerCAmelCase__     )     )

										assert abs(result_mean.item() - 0.3301     ) < 1E-3


				def 		__magic_name__(	self      :List[str]     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.full_loop(thresholding=lowerCAmelCase__      ,      dynamic_thresholding_ratio=0.87      ,      sample_max_value=0.5     )
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		torch.mean(torch.abs(lowerCAmelCase__     )     )

										assert abs(result_mean.item() - 1.1364     ) < 1E-3


				def 		__magic_name__(	self      :Any     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.full_loop(prediction_type='''v_prediction'''     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		torch.mean(torch.abs(lowerCAmelCase__     )     )

										assert abs(result_mean.item() - 0.2251     ) < 1E-3


				def 		__magic_name__(	self      :Optional[int]     )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.full_loop(prediction_type='''v_prediction'''      ,      use_karras_sigmas=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		torch.mean(torch.abs(lowerCAmelCase__     )     )

										assert abs(result_mean.item() - 0.2096     ) < 1E-3


				def 		__magic_name__(	self      :Optional[Any]     )		->      List[str]:
										# make sure that iterating over schedulers with same config names gives same results
										# for defaults
										__SCREAMING_SNAKE_CASE						:					str		      =		DPMSolverMultistepScheduler(**self.get_scheduler_config()     )
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.full_loop(scheduler=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		torch.mean(torch.abs(lowerCAmelCase__     )     )

										assert abs(result_mean.item() - 0.3301     ) < 1E-3

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

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.full_loop(scheduler=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.mean(torch.abs(lowerCAmelCase__     )     )

										assert abs(result_mean.item() - 0.3301     ) < 1E-3


				def 		__magic_name__(	self      :Optional[Any]     )		->      Tuple:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.scheduler_classes[0]
										__SCREAMING_SNAKE_CASE						:					int		      =		self.get_scheduler_config(thresholding=lowerCAmelCase__      ,      dynamic_thresholding_ratio=0     )
										__SCREAMING_SNAKE_CASE						:					str		      =		scheduler_class(**lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		10
										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.dummy_model()
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.dummy_sample_deter.half()
										scheduler.set_timesteps(lowerCAmelCase__     )

										for i, t in enumerate(scheduler.timesteps     ):
																__SCREAMING_SNAKE_CASE						:					str		      =		model(lowerCAmelCase__      ,      lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		scheduler.step(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__     ).prev_sample

										assert sample.dtype == torch.floataa



				def 		__magic_name__(	self      :Optional[int]      ,      **lowerCAmelCase__      :Optional[Any]     )		->      List[str]:
										for scheduler_class in self.scheduler_classes:
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.get_scheduler_config(**lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		scheduler_class(**lowerCAmelCase__     )

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




from __future__ import annotations


def 	_UpperCamelCase							(				lowercase__				):
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		sorted(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					str		      =		len(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)


def 	_UpperCamelCase							(				):
						import doctest

						doctest.testmod()


if __name__ == "__main__":
						main()
