



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						lowerCamelCase__			(		_lowerCamelCase ):
						_lowerCAmelCase             =			(LMSDiscreteScheduler,)
						_lowerCAmelCase             =			10


						def 	_lowerCamelCase      (			self    :	List[str]   ,							**_a    :	Optional[int]      ):
													a__:							int									={
													    "num_train_timesteps": 1_1_0_0,
													    "beta_start": 0.0_0_0_1,
													    "beta_end": 0.0_2,
													    "beta_schedule": "linear",
													}

													config.update(**_UpperCamelCase      )
													return config


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																				self.check_over_configs(num_train_timesteps=_UpperCamelCase      )


						def 	_lowerCamelCase      (			self    :	Dict      ):
													for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]   ,							[0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
																				self.check_over_configs(beta_start=_UpperCamelCase   ,							beta_end=_UpperCamelCase      )


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													for schedule in ["linear", "scaled_linear"]:
																				self.check_over_configs(beta_schedule=_UpperCamelCase      )


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(prediction_type=_UpperCamelCase      )


						def 	_lowerCamelCase      (			self    :	str      ):
													for t in [0, 5_0_0, 8_0_0]:
																				self.check_over_forward(time_step=_UpperCamelCase      )


						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__:							Optional[int]									=self.scheduler_classes[0]
													a__:							Tuple									=self.get_scheduler_config()
													a__:							Dict									=scheduler_class(**_UpperCamelCase      )

													scheduler.set_timesteps(self.num_inference_steps      )

													a__:							Dict									=self.dummy_model()
													a__:							Union[str, Any]									=self.dummy_sample_deter * scheduler.init_noise_sigma

													for i, t in enumerate(scheduler.timesteps      ):
																				a__:							Tuple									=scheduler.scale_model_input(_UpperCamelCase   ,							_UpperCamelCase      )

																				a__:							Tuple									=model(_UpperCamelCase   ,							_UpperCamelCase      )

																				a__:							List[str]									=scheduler.step(_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase      )
																				a__:							List[str]									=output.prev_sample

													a__:							Optional[Any]									=torch.sum(torch.abs(_UpperCamelCase      )      )
													a__:							Union[str, Any]									=torch.mean(torch.abs(_UpperCamelCase      )      )

													assert abs(result_sum.item() - 1_0_0_6.3_8_8      ) < 1e-2
													assert abs(result_mean.item() - 1.3_1      ) < 1e-3


						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__:							List[str]									=self.scheduler_classes[0]
													a__:							List[str]									=self.get_scheduler_config(prediction_type="v_prediction"      )
													a__:							List[Any]									=scheduler_class(**_UpperCamelCase      )

													scheduler.set_timesteps(self.num_inference_steps      )

													a__:							List[Any]									=self.dummy_model()
													a__:							Union[str, Any]									=self.dummy_sample_deter * scheduler.init_noise_sigma

													for i, t in enumerate(scheduler.timesteps      ):
																				a__:							List[Any]									=scheduler.scale_model_input(_UpperCamelCase   ,							_UpperCamelCase      )

																				a__:							Any									=model(_UpperCamelCase   ,							_UpperCamelCase      )

																				a__:							Tuple									=scheduler.step(_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase      )
																				a__:							List[str]									=output.prev_sample

													a__:							Tuple									=torch.sum(torch.abs(_UpperCamelCase      )      )
													a__:							List[Any]									=torch.mean(torch.abs(_UpperCamelCase      )      )

													assert abs(result_sum.item() - 0.0_0_1_7      ) < 1e-2
													assert abs(result_mean.item() - 2.2676e-06      ) < 1e-3


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__:							Dict									=self.scheduler_classes[0]
													a__:							Optional[int]									=self.get_scheduler_config()
													a__:							Union[str, Any]									=scheduler_class(**_UpperCamelCase      )

													scheduler.set_timesteps(self.num_inference_steps   ,							device=_UpperCamelCase      )

													a__:							List[Any]									=self.dummy_model()
													a__:							Any									=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													a__:							List[Any]									=sample.to(_UpperCamelCase      )

													for i, t in enumerate(scheduler.timesteps      ):
																				a__:							str									=scheduler.scale_model_input(_UpperCamelCase   ,							_UpperCamelCase      )

																				a__:							List[Any]									=model(_UpperCamelCase   ,							_UpperCamelCase      )

																				a__:							Optional[Any]									=scheduler.step(_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase      )
																				a__:							Any									=output.prev_sample

													a__:							List[str]									=torch.sum(torch.abs(_UpperCamelCase      )      )
													a__:							Optional[Any]									=torch.mean(torch.abs(_UpperCamelCase      )      )

													assert abs(result_sum.item() - 1_0_0_6.3_8_8      ) < 1e-2
													assert abs(result_mean.item() - 1.3_1      ) < 1e-3







						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							Dict									=self.scheduler_classes[0]
													a__:							str									=self.get_scheduler_config()
													a__:							Optional[int]									=scheduler_class(**_UpperCamelCase   ,							use_karras_sigmas=_UpperCamelCase      )

													scheduler.set_timesteps(self.num_inference_steps   ,							device=_UpperCamelCase      )

													a__:							Any									=self.dummy_model()
													a__:							Tuple									=self.dummy_sample_deter.to(_UpperCamelCase      ) * scheduler.init_noise_sigma
													a__:							Optional[int]									=sample.to(_UpperCamelCase      )

													for t in scheduler.timesteps:
																				a__:							str									=scheduler.scale_model_input(_UpperCamelCase   ,							_UpperCamelCase      )

																				a__:							List[Any]									=model(_UpperCamelCase   ,							_UpperCamelCase      )

																				a__:							Tuple									=scheduler.step(_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase      )
																				a__:							List[Any]									=output.prev_sample

													a__:							Any									=torch.sum(torch.abs(_UpperCamelCase      )      )
													a__:							Any									=torch.mean(torch.abs(_UpperCamelCase      )      )

													assert abs(result_sum.item() - 3_8_1_2.9_9_2_7      ) < 2e-2
													assert abs(result_mean.item() - 4.9_6_4_8      ) < 1e-3












def _a							(      lowerCamelCase:  List[Any]					)      ->		Optional[Any]:







      '''simple docstring'''






      __A			  =							[7_01, 3_01, 1_32, 57, 23, 10, 4, 1]
      for gap in gaps:
            for i in range(lowerCamelCase   ,							len(lowerCamelCase					)					):
                  __A			  =							collection[i]
                  __A			  =							i
                  while j >= gap and collection[j - gap] > insert_value:
                        __A			  =							collection[j - gap]
                        j -= gap
                  if j != i:
                        __A			  =							insert_value
      return collection


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

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





