






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class 	snake_case__ (							_UpperCAmelCase				):
	lowercase__						:					List[str]											=   (DPMSolverSinglestepScheduler,)
	lowercase__						:					List[str]											=   (("num_inference_steps", 25),)
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Dict:
							__magic_name__      :       str       						= {
							    '''num_train_timesteps''': 10_00,
							    '''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''',
							    '''lambda_min_clipped''': -float("""inf"""			),
							    '''variance_type''': None,
							}

							config.update(**_UpperCAmelCase			)
							return config
	def       __magic_name__    (    self      ,       lowerCAmelCase__=0      ,       **lowerCAmelCase__			)							->				str:
							__magic_name__      :       int       						= dict(self.forward_default_kwargs			)
							__magic_name__      :       Optional[Any]       						= kwargs.pop("""num_inference_steps"""      ,       _UpperCAmelCase			)
							__magic_name__      :       Any       						= self.dummy_sample
							__magic_name__      :       Optional[Any]       						= 0.1 * sample
							__magic_name__      :       List[Any]       						= [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

													__magic_name__      :       Dict       						= sample, sample
													for t in range(_UpperCAmelCase      ,       time_step + scheduler.config.solver_order + 1			):
																			__magic_name__      :       List[Any]       						= scheduler.step(_UpperCAmelCase      ,       _UpperCAmelCase      ,       _UpperCAmelCase      ,       **_UpperCAmelCase			).prev_sample
																			__magic_name__      :       str       						= new_scheduler.step(_UpperCAmelCase      ,       _UpperCAmelCase      ,       _UpperCAmelCase      ,       **_UpperCAmelCase			).prev_sample

																			assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"
	def       __magic_name__    (    self			)							->				List[str]:
							pass
	def       __magic_name__    (    self      ,       lowerCAmelCase__=0      ,       **lowerCAmelCase__			)							->				int:
							__magic_name__      :       Union[str, Any]       						= dict(self.forward_default_kwargs			)
							__magic_name__      :       List[Any]       						= kwargs.pop("""num_inference_steps"""      ,       _UpperCAmelCase			)
							__magic_name__      :       List[str]       						= self.dummy_sample
							__magic_name__      :       str       						= 0.1 * sample
							__magic_name__      :       Optional[int]       						= [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

							for scheduler_class in self.scheduler_classes:
													__magic_name__      :       int       						= self.get_scheduler_config()
													__magic_name__      :       Dict       						= scheduler_class(**_UpperCAmelCase			)
													scheduler.set_timesteps(_UpperCAmelCase			)

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(_UpperCAmelCase			)
																			__magic_name__      :       Tuple       						= scheduler_class.from_pretrained(_UpperCAmelCase			)
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(_UpperCAmelCase			)

																			# copy over dummy past residual (must be after setting timesteps)
																			__magic_name__      :       Union[str, Any]       						= dummy_past_residuals[: new_scheduler.config.solver_order]

													__magic_name__      :       List[str]       						= scheduler.step(_UpperCAmelCase      ,       _UpperCAmelCase      ,       _UpperCAmelCase      ,       **_UpperCAmelCase			).prev_sample
													__magic_name__      :       str       						= new_scheduler.step(_UpperCAmelCase      ,       _UpperCAmelCase      ,       _UpperCAmelCase      ,       **_UpperCAmelCase			).prev_sample

													assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"
	def       __magic_name__    (    self      ,       lowerCAmelCase__=None      ,       **lowerCAmelCase__			)							->				Optional[int]:
							if scheduler is None:
													__magic_name__      :       List[Any]       						= self.scheduler_classes[0]
													__magic_name__      :       int       						= self.get_scheduler_config(**_UpperCAmelCase			)
													__magic_name__      :       Dict       						= scheduler_class(**_UpperCAmelCase			)

							__magic_name__      :       Union[str, Any]       						= self.scheduler_classes[0]
							__magic_name__      :       Optional[Any]       						= self.get_scheduler_config(**_UpperCAmelCase			)
							__magic_name__      :       Union[str, Any]       						= scheduler_class(**_UpperCAmelCase			)

							__magic_name__      :       Tuple       						= 10
							__magic_name__      :       Any       						= self.dummy_model()
							__magic_name__      :       str       						= self.dummy_sample_deter
							scheduler.set_timesteps(_UpperCAmelCase			)

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

							return sample
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       Tuple       						= DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
							__magic_name__      :       Any       						= 50
							__magic_name__      :       Dict       						= self.dummy_model()
							__magic_name__      :       Any       						= self.dummy_sample_deter
							scheduler.set_timesteps(_UpperCAmelCase			)

							# make sure that the first t is uneven
							for i, t in enumerate(scheduler.timesteps[3:]			):
													__magic_name__      :       str       						= model(_UpperCAmelCase      ,       _UpperCAmelCase			)
													__magic_name__      :       Union[str, Any]       						= scheduler.step(_UpperCAmelCase      ,       _UpperCAmelCase      ,       _UpperCAmelCase			).prev_sample

							__magic_name__      :       List[str]       						= torch.mean(torch.abs(_UpperCAmelCase			)			)

							assert abs(result_mean.item() - 0.2_5_7_4			) < 1e-3
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							for timesteps in [25, 50, 1_00, 9_99, 10_00]:
													self.check_over_configs(num_train_timesteps=_UpperCAmelCase			)
	def       __magic_name__    (    self			)							->				List[Any]:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							__magic_name__      :       Optional[Any]       						= DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
							__magic_name__      :       Union[str, Any]       						= self.full_loop(scheduler=_UpperCAmelCase			)
							__magic_name__      :       Dict       						= torch.mean(torch.abs(_UpperCAmelCase			)			)

							assert abs(result_mean.item() - 0.2_7_9_1			) < 1e-3

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

							__magic_name__      :       Union[str, Any]       						= self.full_loop(scheduler=_UpperCAmelCase			)
							__magic_name__      :       Dict       						= torch.mean(torch.abs(_UpperCAmelCase			)			)

							assert abs(result_mean.item() - 0.2_7_9_1			) < 1e-3
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							self.check_over_configs(thresholding=_UpperCAmelCase			)
							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=_UpperCAmelCase      ,       prediction_type=_UpperCAmelCase      ,       sample_max_value=_UpperCAmelCase      ,       algorithm_type="""dpmsolver++"""      ,       solver_order=_UpperCAmelCase      ,       solver_type=_UpperCAmelCase      ,       )
	def       __magic_name__    (    self			)							->				Optional[int]:
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=_UpperCAmelCase			)
	def       __magic_name__    (    self			)							->				List[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=_UpperCAmelCase      ,       solver_type=_UpperCAmelCase      ,       prediction_type=_UpperCAmelCase      ,       algorithm_type=_UpperCAmelCase      ,       )
																															__magic_name__      :       int       						= self.full_loop(
																															    solver_order=_UpperCAmelCase      ,       solver_type=_UpperCAmelCase      ,       prediction_type=_UpperCAmelCase      ,       algorithm_type=_UpperCAmelCase      ,       )
																															assert not torch.isnan(_UpperCAmelCase			).any(), "Samples have nan numbers"
	def       __magic_name__    (    self			)							->				Dict:
							self.check_over_configs(lower_order_final=_UpperCAmelCase			)
							self.check_over_configs(lower_order_final=_UpperCAmelCase			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							self.check_over_configs(lambda_min_clipped=-float("""inf"""			)			)
							self.check_over_configs(lambda_min_clipped=-5.1			)
	def       __magic_name__    (    self			)							->				int:
							self.check_over_configs(variance_type=_UpperCAmelCase			)
							self.check_over_configs(variance_type="""learned_range"""			)
	def       __magic_name__    (    self			)							->				Tuple:
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
													self.check_over_forward(num_inference_steps=_UpperCAmelCase      ,       time_step=0			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       Optional[int]       						= self.full_loop()
							__magic_name__      :       Tuple       						= torch.mean(torch.abs(_UpperCAmelCase			)			)

							assert abs(result_mean.item() - 0.2_7_9_1			) < 1e-3
	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       Union[str, Any]       						= self.full_loop(use_karras_sigmas=_UpperCAmelCase			)
							__magic_name__      :       List[Any]       						= torch.mean(torch.abs(_UpperCAmelCase			)			)

							assert abs(result_mean.item() - 0.2_2_4_8			) < 1e-3
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Tuple       						= self.full_loop(prediction_type="""v_prediction"""			)
							__magic_name__      :       Tuple       						= torch.mean(torch.abs(_UpperCAmelCase			)			)

							assert abs(result_mean.item() - 0.1_4_5_3			) < 1e-3
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       List[str]       						= self.full_loop(prediction_type="""v_prediction"""      ,       use_karras_sigmas=_UpperCAmelCase			)
							__magic_name__      :       Any       						= torch.mean(torch.abs(_UpperCAmelCase			)			)

							assert abs(result_mean.item() - 0.0_6_4_9			) < 1e-3







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

							__magic_name__      :       Any       						= 10
							__magic_name__      :       str       						= self.dummy_model()
							__magic_name__      :       Any       						= self.dummy_sample_deter.half()
							scheduler.set_timesteps(_UpperCAmelCase			)

							for i, t in enumerate(scheduler.timesteps			):
													__magic_name__      :       Any       						= model(_UpperCAmelCase      ,       _UpperCAmelCase			)
													__magic_name__      :       str       						= scheduler.step(_UpperCAmelCase      ,       _UpperCAmelCase      ,       _UpperCAmelCase			).prev_sample

							assert sample.dtype == torch.floataa








__magic_name__:					List[Any]							=     "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__magic_name__:					Dict							=     [{"type": "code", "content": INSTALL_CONTENT}]
__magic_name__:					Optional[Any]							=     {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}
