






"""simple docstring"""




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler
class 							A_      (  unittest.TestCase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Tuple	=   (ScoreSdeVeScheduler,)
 _SCREAMING_SNAKE_CASE							:     Union[str, Any]	=   ()


 @property
 def 	snake_case__			(		self)	->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase			: Any     					=			4
     _UpperCAmelCase			: Optional[int]     					=			3
     _UpperCAmelCase			: Union[str, Any]     					=			8
     _UpperCAmelCase			: List[str]     					=			8

     _UpperCAmelCase			: int     					=			torch.rand((batch_size, num_channels, height, width))

     return sample


 @property
 def 	snake_case__			(		self)	->						Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase			: Tuple     					=			4
     _UpperCAmelCase			: str     					=			3
     _UpperCAmelCase			: Optional[Any]     					=			8
     _UpperCAmelCase			: Dict     					=			8

     _UpperCAmelCase			: Dict     					=			batch_size * num_channels * height * width
     _UpperCAmelCase			: Union[str, Any]     					=			torch.arange(_A)
     _UpperCAmelCase			: Tuple     					=			sample.reshape(_A							,					_A							,					_A							,					_A)
     _UpperCAmelCase			: Optional[int]     					=			sample / num_elems
     _UpperCAmelCase			: int     					=			sample.permute(3							,					0							,					1							,					2)

     return sample


 def 	snake_case__			(		self)	->						Union[str, Any]:

     """simple docstring"""



     def model(_A							,					_A							,					*_A):
         return sample * t / (t + 1)

     return model


 def 	snake_case__			(		self							,					**_A)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: Dict     					=			{
         '''num_train_timesteps''': 2000,
         '''snr''': 0.15,
         '''sigma_min''': 0.01,
         '''sigma_max''': 1348,
         '''sampling_eps''': 1e-5,
     }

     config.update(**_A)
     return config


 def 	snake_case__			(		self							,					_A=0							,					**_A)	->						Dict:

     """simple docstring"""



     _UpperCAmelCase			: Any     					=			dict(self.forward_default_kwargs)

     for scheduler_class in self.scheduler_classes:
         _UpperCAmelCase			: List[Any]     					=			self.dummy_sample
         _UpperCAmelCase			: Optional[int]     					=			0.1 * sample

         _UpperCAmelCase			: List[Any]     					=			self.get_scheduler_config(**_A)
         _UpperCAmelCase			: List[str]     					=			scheduler_class(**_A)

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(_A)
             _UpperCAmelCase			: List[str]     					=			scheduler_class.from_pretrained(_A)

         _UpperCAmelCase			: Union[str, Any]     					=			scheduler.step_pred(
             _A							,					_A							,					_A							,					generator=torch.manual_seed(0)							,					**_A).prev_sample
         _UpperCAmelCase			: Dict     					=			new_scheduler.step_pred(
             _A							,					_A							,					_A							,					generator=torch.manual_seed(0)							,					**_A).prev_sample

         assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"

         _UpperCAmelCase			: List[Any]     					=			scheduler.step_correct(_A							,					_A							,					generator=torch.manual_seed(0)							,					**_A).prev_sample
         _UpperCAmelCase			: Union[str, Any]     					=			new_scheduler.step_correct(
             _A							,					_A							,					generator=torch.manual_seed(0)							,					**_A).prev_sample

         assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler correction are not identical"


 def 	snake_case__			(		self							,					_A=0							,					**_A)	->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase			: Optional[int]     					=			dict(self.forward_default_kwargs)
     kwargs.update(_A)

     for scheduler_class in self.scheduler_classes:
         _UpperCAmelCase			: Any     					=			self.dummy_sample
         _UpperCAmelCase			: int     					=			0.1 * sample

         _UpperCAmelCase			: List[Any]     					=			self.get_scheduler_config()
         _UpperCAmelCase			: str     					=			scheduler_class(**_A)

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(_A)
             _UpperCAmelCase			: Dict     					=			scheduler_class.from_pretrained(_A)

         _UpperCAmelCase			: Tuple     					=			scheduler.step_pred(
             _A							,					_A							,					_A							,					generator=torch.manual_seed(0)							,					**_A).prev_sample
         _UpperCAmelCase			: int     					=			new_scheduler.step_pred(
             _A							,					_A							,					_A							,					generator=torch.manual_seed(0)							,					**_A).prev_sample

         assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"

         _UpperCAmelCase			: List[str]     					=			scheduler.step_correct(_A							,					_A							,					generator=torch.manual_seed(0)							,					**_A).prev_sample
         _UpperCAmelCase			: Union[str, Any]     					=			new_scheduler.step_correct(
             _A							,					_A							,					generator=torch.manual_seed(0)							,					**_A).prev_sample

         assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler correction are not identical"


 def 	snake_case__			(		self)	->						Dict:

     """simple docstring"""



     for timesteps in [10, 100, 1000]:
         self.check_over_configs(num_train_timesteps=_A)


 def 	snake_case__			(		self)	->						Optional[int]:

     """simple docstring"""



     for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]							,					[1, 100, 1000]):
         self.check_over_configs(sigma_min=_A							,					sigma_max=_A)


 def 	snake_case__			(		self)	->						List[Any]:

     """simple docstring"""



     for t in [0.1, 0.5, 0.75]:
         self.check_over_forward(time_step=_A)


 def 	snake_case__			(		self)	->						Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase			: List[str]     					=			dict(self.forward_default_kwargs)

     _UpperCAmelCase			: Dict     					=			self.scheduler_classes[0]
     _UpperCAmelCase			: Dict     					=			self.get_scheduler_config()
     _UpperCAmelCase			: str     					=			scheduler_class(**_A)

     _UpperCAmelCase			: Dict     					=			3

     _UpperCAmelCase			: Tuple     					=			self.dummy_model()
     _UpperCAmelCase			: Tuple     					=			self.dummy_sample_deter

     scheduler.set_sigmas(_A)
     scheduler.set_timesteps(_A)
     _UpperCAmelCase			: List[str]     					=			torch.manual_seed(0)

     for i, t in enumerate(scheduler.timesteps):
         _UpperCAmelCase			: Optional[Any]     					=			scheduler.sigmas[i]

         for _ in range(scheduler.config.correct_steps):
             with torch.no_grad():
                 _UpperCAmelCase			: Tuple     					=			model(_A							,					_A)
             _UpperCAmelCase			: Dict     					=			scheduler.step_correct(_A							,					_A							,					generator=_A							,					**_A).prev_sample

         with torch.no_grad():
             _UpperCAmelCase			: str     					=			model(_A							,					_A)

         _UpperCAmelCase			: List[Any]     					=			scheduler.step_pred(_A							,					_A							,					_A							,					generator=_A							,					**_A)
         _UpperCAmelCase			: Union[str, Any]     					=			output.prev_sample, output.prev_sample_mean

     _UpperCAmelCase			: Dict     					=			torch.sum(torch.abs(_A))
     _UpperCAmelCase			: List[str]     					=			torch.mean(torch.abs(_A))

     assert np.isclose(result_sum.item()							,					14372758528.0)
     assert np.isclose(result_mean.item()							,					18714530.0)


 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



     _UpperCAmelCase			: Union[str, Any]     					=			dict(self.forward_default_kwargs)

     _UpperCAmelCase			: List[str]     					=			kwargs.pop('''num_inference_steps'''							,					_A)

     for scheduler_class in self.scheduler_classes:
         _UpperCAmelCase			: Optional[Any]     					=			self.get_scheduler_config()
         _UpperCAmelCase			: Union[str, Any]     					=			scheduler_class(**_A)

         _UpperCAmelCase			: Optional[int]     					=			self.dummy_sample
         _UpperCAmelCase			: Optional[Any]     					=			0.1 * sample

         if num_inference_steps is not None and hasattr(_A							,					'''set_timesteps'''):
             scheduler.set_timesteps(_A)
         elif num_inference_steps is not None and not hasattr(_A							,					'''set_timesteps'''):
             _UpperCAmelCase			: int     					=			num_inference_steps

         _UpperCAmelCase			: str     					=			scheduler.step_pred(_A							,					0							,					_A							,					generator=torch.manual_seed(0)							,					**_A).prev_sample
         _UpperCAmelCase			: str     					=			scheduler.step_pred(_A							,					1							,					_A							,					generator=torch.manual_seed(0)							,					**_A).prev_sample

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












def        _lowerCamelCase		(  __A      :							list	) ->   list:
    _UpperCAmelCase			: Dict     					=			1.3
    _UpperCAmelCase			: Any     					=			len(__A	)
    _UpperCAmelCase			: List[Any]     					=			False

    while not completed:
        # Update the gap value for a next comb
        _UpperCAmelCase			: Optional[Any]     					=			int(gap / shrink_factor	)
        if gap <= 1:
            _UpperCAmelCase			: Any     					=			True

        _UpperCAmelCase			: Optional[Any]     					=			0
        while index + gap < len(__A	):
            if data[index] > data[index + gap]:
                # Swap values
                _UpperCAmelCase					,	_UpperCAmelCase			: Union[str, Any]     					=			data[index + gap], data[index]
                _UpperCAmelCase			: Any     					=			False
            index += 1

    return data


if __name__ == "__main__":
  import doctest

  doctest.testmod()

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




