

"""simple docstring"""






from __future__ import annotations



def   lowercase_	(    _lowercase       :		list[int | str]	):




					'''simple docstring'''






					create_state_space_tree(_lowercase							,      []							,      0							,      [0 for i in range(len(_lowercase	)	)]	)



def   lowercase_	(    _lowercase       :		list[int | str]							,      _lowercase       :		list[int | str]							,      _lowercase       :		int							,      _lowercase       :		list[int]							,      ):




					'''simple docstring'''






					if index == len(_lowercase	):
										print(_lowercase	)
										return

					for i in range(len(_lowercase	)	):
										if not index_used[i]:
															current_sequence.append(sequence[i]	)
															UpperCAmelCase			:   List[str]      =				True
															create_state_space_tree(_lowercase							,      _lowercase							,      index + 1							,      _lowercase	)
															current_sequence.pop()
															UpperCAmelCase			:   List[Any]      =				False
snake_case_   :   list[int | str]        =       [3, 1, 2, 4]
generate_all_permutations(sequence)

snake_case_   :   list[int | str]        =       ["A", "B", "C"]




generate_all_permutations(sequence_a)





"""simple docstring"""






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						(UniPCMultistepScheduler,)
			SCREAMING_SNAKE_CASE__     				=						(('''num_inference_steps''', 25),)







			def 			__lowerCAmelCase				(			self			: Tuple			,  **lowercase			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				{
								    "num_train_timesteps": 10_00,
								    "beta_start": 0.0_0_0_1,
								    "beta_end": 0.0_2,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								    "solver_type": "bh2",
								}

								config.update(**lowercase  )
								return config







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: List[str]=0			,  **lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				dict(self.forward_default_kwargs  )
								UpperCAmelCase			:   Dict      =				kwargs.pop("num_inference_steps"			,  lowercase  )
								UpperCAmelCase			:   str      =				self.dummy_sample
								UpperCAmelCase			:   Tuple      =				0.1 * sample
								UpperCAmelCase			:   int      =				[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(**lowercase  )
													UpperCAmelCase			:   Tuple      =				scheduler_class(**lowercase  )
													scheduler.set_timesteps(lowercase  )
													# copy over dummy past residuals
													UpperCAmelCase			:   Tuple      =				dummy_past_residuals[: scheduler.config.solver_order]

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

													UpperCAmelCase      ,							UpperCAmelCase			:   List[Any]      =				sample, sample
													for t in range(lowercase			,  time_step + scheduler.config.solver_order + 1  ):
																		UpperCAmelCase			:   List[str]      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample
																		UpperCAmelCase			:   List[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 			__lowerCAmelCase				(			self			: int			,  lowercase			: int=0			,  **lowercase			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				dict(self.forward_default_kwargs  )
								UpperCAmelCase			:   Union[str, Any]      =				kwargs.pop("num_inference_steps"			,  lowercase  )
								UpperCAmelCase			:   Tuple      =				self.dummy_sample
								UpperCAmelCase			:   Any      =				0.1 * sample
								UpperCAmelCase			:   int      =				[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()
													UpperCAmelCase			:   List[Any]      =				scheduler_class(**lowercase  )
													scheduler.set_timesteps(lowercase  )

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(lowercase  )
																		UpperCAmelCase			:   str      =				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)
																		UpperCAmelCase			:   Tuple      =				dummy_past_residuals[: new_scheduler.config.solver_order]

													UpperCAmelCase			:   Dict      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample
													UpperCAmelCase			:   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 			__lowerCAmelCase				(			self			: Union[str, Any]			,  lowercase			: Optional[int]=None			,  **lowercase			: int  ):





								'''simple docstring'''



								if scheduler is None:
													UpperCAmelCase			:   Dict      =				self.scheduler_classes[0]
													UpperCAmelCase			:   int      =				self.get_scheduler_config(**lowercase  )
													UpperCAmelCase			:   str      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   Optional[int]      =				self.scheduler_classes[0]
								UpperCAmelCase			:   Dict      =				self.get_scheduler_config(**lowercase  )
								UpperCAmelCase			:   int      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   List[str]      =				10
								UpperCAmelCase			:   Optional[Any]      =				self.dummy_model()
								UpperCAmelCase			:   Union[str, Any]      =				self.dummy_sample_deter
								scheduler.set_timesteps(lowercase  )

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

								return sample







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				dict(self.forward_default_kwargs  )

								UpperCAmelCase			:   List[Any]      =				kwargs.pop("num_inference_steps"			,  lowercase  )

								for scheduler_class in self.scheduler_classes:
													UpperCAmelCase			:   str      =				self.get_scheduler_config()
													UpperCAmelCase			:   Dict      =				scheduler_class(**lowercase  )

													UpperCAmelCase			:   Any      =				self.dummy_sample
													UpperCAmelCase			:   Any      =				0.1 * sample

													if num_inference_steps is not None and hasattr(lowercase			,  "set_timesteps"  ):
																		scheduler.set_timesteps(lowercase  )
													elif num_inference_steps is not None and not hasattr(lowercase			,  "set_timesteps"  ):
																		UpperCAmelCase			:   str      =				num_inference_steps

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

													UpperCAmelCase			:   str      =				scheduler.timesteps[5]
													UpperCAmelCase			:   List[Any]      =				scheduler.timesteps[6]

													UpperCAmelCase			:   Union[str, Any]      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample
													UpperCAmelCase			:   int      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample

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







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				UniPCMultistepScheduler(**self.get_scheduler_config()  )
								UpperCAmelCase			:   Tuple      =				self.full_loop(scheduler=lowercase  )
								UpperCAmelCase			:   Any      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.2_4_6_4  ) < 1E-3

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

								UpperCAmelCase			:   Dict      =				self.full_loop(scheduler=lowercase  )
								UpperCAmelCase			:   Any      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.2_4_6_4  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								for timesteps in [25, 50, 1_00, 9_99, 10_00]:
													self.check_over_configs(num_train_timesteps=lowercase  )







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								self.check_over_configs(thresholding=lowercase  )
								for order in [1, 2, 3]:
													for solver_type in ["bh1", "bh2"]:
																		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			,  solver_order=lowercase			,  solver_type=lowercase			,  )







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowercase  )







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								for solver_type in ["bh1", "bh2"]:
													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			,  )
																							UpperCAmelCase			:   Union[str, Any]      =				self.full_loop(
																							    solver_order=lowercase			,  solver_type=lowercase			,  prediction_type=lowercase			,  )
																							assert not torch.isnan(lowercase  ).any(), "Samples have nan numbers"







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								self.check_over_configs(lower_order_final=lowercase  )
								self.check_over_configs(lower_order_final=lowercase  )







			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
													self.check_over_forward(num_inference_steps=lowercase			,  time_step=0  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.full_loop()
								UpperCAmelCase			:   Any      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.2_4_6_4  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.full_loop(prediction_type="v_prediction"  )
								UpperCAmelCase			:   int      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.1_0_1_4  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.scheduler_classes[0]
								UpperCAmelCase			:   int      =				self.get_scheduler_config(thresholding=lowercase			,  dynamic_thresholding_ratio=0  )
								UpperCAmelCase			:   Tuple      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   Any      =				10
								UpperCAmelCase			:   Dict      =				self.dummy_model()
								UpperCAmelCase			:   List[str]      =				self.dummy_sample_deter.half()
								scheduler.set_timesteps(lowercase  )

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

								assert sample.dtype == torch.floataa







			def 			__lowerCAmelCase				(			self			: List[str]			,  **lowercase			: Optional[int]  ):





								'''simple docstring'''



								for scheduler_class in self.scheduler_classes:
													UpperCAmelCase			:   Union[str, Any]      =				self.get_scheduler_config(**lowercase  )
													UpperCAmelCase			:   Union[str, Any]      =				scheduler_class(**lowercase  )

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


