






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		snake_case			 = (LMSDiscreteScheduler,)
		snake_case			 = 1_0
		def    A(	self		:							List[Any]    ,**_SCREAMING_SNAKE_CASE		:							str		)     -> List[str]:


						'''simple docstring'''
						A									=   {
						    'num_train_timesteps': 1_1_0_0,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**a__		)
						return config
		def    A(	self		:							List[str]		)     -> List[str]:


						'''simple docstring'''
						for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
										self.check_over_configs(num_train_timesteps=a__		)
		def    A(	self		:							int		)     -> str:


						'''simple docstring'''
						for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]    ,[0.0002, 0.002, 0.02]		):
										self.check_over_configs(beta_start=a__    ,beta_end=a__		)
		def    A(	self		:							Union[str, Any]		)     -> Optional[Any]:


						'''simple docstring'''
						for schedule in ["linear", "scaled_linear"]:
										self.check_over_configs(beta_schedule=a__		)
		def    A(	self		:							List[str]		)     -> Any:


						'''simple docstring'''
						for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=a__		)
		def    A(	self		:							Dict		)     -> Union[str, Any]:


						'''simple docstring'''
						for t in [0, 5_0_0, 8_0_0]:
										self.check_over_forward(time_step=a__		)
		def    A(	self		:							int		)     -> int:


						'''simple docstring'''
						A									=   self.scheduler_classes[0]
						A									=   self.get_scheduler_config()
						A									=   scheduler_class(**a__		)

						scheduler.set_timesteps(self.num_inference_steps		)

						A									=   self.dummy_model()
						A									=   self.dummy_sample_deter * scheduler.init_noise_sigma

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

										A									=   model(a__    ,a__		)

										A									=   scheduler.step(a__    ,a__    ,a__		)
										A									=   output.prev_sample

						A									=   torch.sum(torch.abs(a__		)		)
						A									=   torch.mean(torch.abs(a__		)		)

						assert abs(result_sum.item() - 1_0_0_6.3_8_8		) < 1E-2
						assert abs(result_mean.item() - 1.31		) < 1E-3
		def    A(	self		:							str		)     -> Tuple:


						'''simple docstring'''
						A									=   self.scheduler_classes[0]
						A									=   self.get_scheduler_config(prediction_type='v_prediction'		)
						A									=   scheduler_class(**a__		)

						scheduler.set_timesteps(self.num_inference_steps		)

						A									=   self.dummy_model()
						A									=   self.dummy_sample_deter * scheduler.init_noise_sigma

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

										A									=   model(a__    ,a__		)

										A									=   scheduler.step(a__    ,a__    ,a__		)
										A									=   output.prev_sample

						A									=   torch.sum(torch.abs(a__		)		)
						A									=   torch.mean(torch.abs(a__		)		)

						assert abs(result_sum.item() - 0.0017		) < 1E-2
						assert abs(result_mean.item() - 2.2676E-06		) < 1E-3
		def    A(	self		:							int		)     -> Optional[int]:


						'''simple docstring'''
						A									=   self.scheduler_classes[0]
						A									=   self.get_scheduler_config()
						A									=   scheduler_class(**a__		)

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

						A									=   self.dummy_model()
						A									=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						A									=   sample.to(a__		)

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

										A									=   model(a__    ,a__		)

										A									=   scheduler.step(a__    ,a__    ,a__		)
										A									=   output.prev_sample

						A									=   torch.sum(torch.abs(a__		)		)
						A									=   torch.mean(torch.abs(a__		)		)

						assert abs(result_sum.item() - 1_0_0_6.3_8_8		) < 1E-2
						assert abs(result_mean.item() - 1.31		) < 1E-3







		def    A(	self		:							List[str]		)     -> List[Any]:


						'''simple docstring'''
						A									=   self.scheduler_classes[0]
						A									=   self.get_scheduler_config()
						A									=   scheduler_class(**a__    ,use_karras_sigmas=a__		)

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

						A									=   self.dummy_model()
						A									=   self.dummy_sample_deter.to(a__		) * scheduler.init_noise_sigma
						A									=   sample.to(a__		)

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

										A									=   model(a__    ,a__		)

										A									=   scheduler.step(a__    ,a__    ,a__		)
										A									=   output.prev_sample

						A									=   torch.sum(torch.abs(a__		)		)
						A									=   torch.mean(torch.abs(a__		)		)

						assert abs(result_sum.item() - 3_8_1_2.9_9_2_7		) < 2E-2
						assert abs(result_mean.item() - 4.9648		) < 1E-3












"""simple docstring"""






def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							Dict:






       """simple docstring"""




       _UpperCAmelCase			     =  [701, 301, 132, 57, 23, 10, 4, 1]
       for gap in gaps:
              for i in range(SCREAMING_SNAKE_CASE,len(SCREAMING_SNAKE_CASE				)				):
                     _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))






