





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class   _UpperCAmelCase	(   _lowercase					):
					__A          =   (DPMSolverMultistepScheduler,)
					__A          =   (("num_inference_steps", 25),)




					def 	snake_case_		(						self    ,      **__lowerCAmelCase       )						->  int:
							UpperCamelCase__         =	{
							    """num_train_timesteps""": 1_000,
							    """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    ,      __lowerCAmelCase=0    ,      **__lowerCAmelCase       )						->  Tuple:
							UpperCamelCase__         =	dict(self.forward_default_kwargs       )
							UpperCamelCase__         =	kwargs.pop("""num_inference_steps"""    ,      __lowerCAmelCase       )
							UpperCamelCase__         =	self.dummy_sample
							UpperCamelCase__         =	0.1 * sample
							UpperCamelCase__         =	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

							for scheduler_class in self.scheduler_classes:
									UpperCamelCase__         =	self.get_scheduler_config(**__lowerCAmelCase       )
									UpperCamelCase__         =	scheduler_class(**__lowerCAmelCase       )
									scheduler.set_timesteps(__lowerCAmelCase       )
									# copy over dummy past residuals
									UpperCamelCase__         =	dummy_past_residuals[: scheduler.config.solver_order]

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

									UpperCamelCase__  ,			UpperCamelCase__         =	sample, sample
									for t in range(__lowerCAmelCase    ,      time_step + scheduler.config.solver_order + 1       ):
											UpperCamelCase__         =	scheduler.step(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      **__lowerCAmelCase       ).prev_sample
											UpperCamelCase__         =	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       )						->  List[Any]:
							pass




					def 	snake_case_		(						self    ,      __lowerCAmelCase=0    ,      **__lowerCAmelCase       )						->  Any:
							UpperCamelCase__         =	dict(self.forward_default_kwargs       )
							UpperCamelCase__         =	kwargs.pop("""num_inference_steps"""    ,      __lowerCAmelCase       )
							UpperCamelCase__         =	self.dummy_sample
							UpperCamelCase__         =	0.1 * sample
							UpperCamelCase__         =	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

							for scheduler_class in self.scheduler_classes:
									UpperCamelCase__         =	self.get_scheduler_config()
									UpperCamelCase__         =	scheduler_class(**__lowerCAmelCase       )
									scheduler.set_timesteps(__lowerCAmelCase       )

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

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(__lowerCAmelCase       )
											UpperCamelCase__         =	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__         =	dummy_past_residuals[: new_scheduler.config.solver_order]

									UpperCamelCase__         =	scheduler.step(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      **__lowerCAmelCase       ).prev_sample
									UpperCamelCase__         =	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    ,      __lowerCAmelCase=None    ,      **__lowerCAmelCase       )						->  Union[str, Any]:
							if scheduler is None:
									UpperCamelCase__         =	self.scheduler_classes[0]
									UpperCamelCase__         =	self.get_scheduler_config(**__lowerCAmelCase       )
									UpperCamelCase__         =	scheduler_class(**__lowerCAmelCase       )

							UpperCamelCase__         =	10
							UpperCamelCase__         =	self.dummy_model()
							UpperCamelCase__         =	self.dummy_sample_deter
							scheduler.set_timesteps(__lowerCAmelCase       )

							for i, t in enumerate(scheduler.timesteps       ):
									UpperCamelCase__         =	model(__lowerCAmelCase    ,      __lowerCAmelCase       )
									UpperCamelCase__         =	scheduler.step(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       ).prev_sample

							return sample




					def 	snake_case_		(						self       )						->  Tuple:
							UpperCamelCase__         =	dict(self.forward_default_kwargs       )

							UpperCamelCase__         =	kwargs.pop("""num_inference_steps"""    ,      __lowerCAmelCase       )

							for scheduler_class in self.scheduler_classes:
									UpperCamelCase__         =	self.get_scheduler_config()
									UpperCamelCase__         =	scheduler_class(**__lowerCAmelCase       )

									UpperCamelCase__         =	self.dummy_sample
									UpperCamelCase__         =	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__         =	num_inference_steps

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

									UpperCamelCase__         =	scheduler.timesteps[5]
									UpperCamelCase__         =	scheduler.timesteps[6]

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




					def 	snake_case_		(						self       )						->  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 	snake_case_		(						self       )						->  List[str]:
							for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=__lowerCAmelCase       )




					def 	snake_case_		(						self       )						->  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    ,      )
															UpperCamelCase__         =	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       )						->  Dict:
							self.check_over_configs(lower_order_final=__lowerCAmelCase       )
							self.check_over_configs(lower_order_final=__lowerCAmelCase       )




					def 	snake_case_		(						self       )						->  Dict:
							self.check_over_configs(lambda_min_clipped=-float("""inf"""       )       )
							self.check_over_configs(lambda_min_clipped=-5.1       )




					def 	snake_case_		(						self       )						->  Tuple:
							self.check_over_configs(variance_type=__lowerCAmelCase       )
							self.check_over_configs(variance_type="""learned_range"""       )




					def 	snake_case_		(						self       )						->  int:
							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 	snake_case_		(						self       )						->  Optional[Any]:
							UpperCamelCase__         =	self.full_loop()
							UpperCamelCase__         =	torch.mean(torch.abs(__lowerCAmelCase       )       )

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




					def 	snake_case_		(						self       )						->  Optional[Any]:
							UpperCamelCase__         =	self.full_loop(thresholding=__lowerCAmelCase    ,      dynamic_thresholding_ratio=0.8_7    ,      sample_max_value=0.5       )
							UpperCamelCase__         =	torch.mean(torch.abs(__lowerCAmelCase       )       )

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




					def 	snake_case_		(						self       )						->  str:
							UpperCamelCase__         =	self.full_loop(prediction_type="""v_prediction"""       )
							UpperCamelCase__         =	torch.mean(torch.abs(__lowerCAmelCase       )       )

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




					def 	snake_case_		(						self       )						->  Dict:
							UpperCamelCase__         =	self.full_loop(prediction_type="""v_prediction"""    ,      use_karras_sigmas=__lowerCAmelCase       )
							UpperCamelCase__         =	torch.mean(torch.abs(__lowerCAmelCase       )       )

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




					def 	snake_case_		(						self       )						->  Tuple:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							UpperCamelCase__         =	DPMSolverMultistepScheduler(**self.get_scheduler_config()       )
							UpperCamelCase__         =	self.full_loop(scheduler=__lowerCAmelCase       )
							UpperCamelCase__         =	torch.mean(torch.abs(__lowerCAmelCase       )       )

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

							UpperCamelCase__         =	DPMSolverSinglestepScheduler.from_config(scheduler.config       )
							UpperCamelCase__         =	UniPCMultistepScheduler.from_config(scheduler.config       )
							UpperCamelCase__         =	DEISMultistepScheduler.from_config(scheduler.config       )
							UpperCamelCase__         =	DPMSolverMultistepScheduler.from_config(scheduler.config       )

							UpperCamelCase__         =	self.full_loop(scheduler=__lowerCAmelCase       )
							UpperCamelCase__         =	torch.mean(torch.abs(__lowerCAmelCase       )       )

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




					def 	snake_case_		(						self       )						->  List[Any]:
							UpperCamelCase__         =	self.scheduler_classes[0]
							UpperCamelCase__         =	self.get_scheduler_config(thresholding=__lowerCAmelCase    ,      dynamic_thresholding_ratio=0       )
							UpperCamelCase__         =	scheduler_class(**__lowerCAmelCase       )

							UpperCamelCase__         =	10
							UpperCamelCase__         =	self.dummy_model()
							UpperCamelCase__         =	self.dummy_sample_deter.half()
							scheduler.set_timesteps(__lowerCAmelCase       )

							for i, t in enumerate(scheduler.timesteps       ):
									UpperCamelCase__         =	model(__lowerCAmelCase    ,      __lowerCAmelCase       )
									UpperCamelCase__         =	scheduler.step(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       ).prev_sample

							assert sample.dtype == torch.floataa




					def 	snake_case_		(						self    ,      **__lowerCAmelCase       )						->  Any:
							for scheduler_class in self.scheduler_classes:
									UpperCamelCase__         =	self.get_scheduler_config(**__lowerCAmelCase       )
									UpperCamelCase__         =	scheduler_class(**__lowerCAmelCase       )

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












def     a						(      _lowercase   :     List[str]       )     ->List[str]:
		UpperCamelCase__         =	[7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
		for gap in gaps:
				for i in range(_lowercase ,						len(_lowercase       )       ):
						UpperCamelCase__         =	collection[i]
						UpperCamelCase__         =	i
						while j >= gap and collection[j - gap] > insert_value:
								UpperCamelCase__         =	collection[j - gap]
								j -= gap
						if j != i:
								UpperCamelCase__         =	insert_value
		return collection


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				__lowerCAmelCase     =			input("Enter numbers separated by a comma:\n").strip()
				__lowerCAmelCase     =			[int(item) for item in user_input.split(",")]
				print(shell_sort(unsorted))





