code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import torch
  import torch.nn.functional as F
  
  from diffusers import VQDiffusionScheduler
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     Optional[int]				  =				(VQDiffusionScheduler,)
  
  
  
        def 		snake_case_      (  self  :			List[str]  ,			**A  :			Union[str, Any]		):
             _UpperCAmelCase    :			str              =					{
                 "num_vec_classes": 4_0_9_7,
                 "num_train_timesteps": 1_0_0,
             }
  
             config.update(**A		)
             return config
  
  
  
        def 		snake_case_      (  self  :			Any  ,			A  :			Optional[int]		):
             _UpperCAmelCase    :			Optional[int]              =					4
             _UpperCAmelCase    :			Tuple              =					8
             _UpperCAmelCase    :			Tuple              =					8
  
             _UpperCAmelCase    :			Tuple              =					torch.randint(0  ,			A  ,			(batch_size, height * width)		)
  
             return sample
  
  
  
        @property
        def 		snake_case_      (  self  :			List[Any]		):
             assert False
  
  
  
        def 		snake_case_      (  self  :			int  ,			A  :			int		):
             def model(A  :			List[str]  ,			A  :			List[str]  ,			*A  :			int		):
                  _UpperCAmelCase       , _UpperCAmelCase    :			List[Any]              =					sample.shape
                  _UpperCAmelCase    :			Tuple              =					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)		)
                  _UpperCAmelCase    :			Union[str, Any]              =					F.log_softmax(logits.double()  ,			dim=1		).float()
                  return return_value
  
             return model
  
  
  
        def 		snake_case_      (  self  :			Union[str, Any]		):
             for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
                  self.check_over_configs(num_train_timesteps=A		)
  
  
  
        def 		snake_case_      (  self  :			List[Any]		):
             for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
                  self.check_over_configs(num_vec_classes=A		)
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]		):
             for t in [0, 5_0, 9_9]:
                  self.check_over_forward(time_step=A		)
  
  
  
        def 		snake_case_      (  self  :			str		):
             pass
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     None:
  
  
  
  
  
       '''simple docstring'''
  
       if right <= left:
            return
       _UpperCAmelCase    :			Any              =					left
       _UpperCAmelCase    :			Tuple              =					right
       _UpperCAmelCase    :			List[Any]              =					sorting[left]
       while i <= b:
            if sorting[i] < pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			List[Any]              =					sorting[i], sorting[a]
                 a += 1
                 i += 1
            elif sorting[i] > pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			Any              =					sorting[i], sorting[b]
                 b -= 1
            else:
                 i += 1
       quick_sort_apartition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						a - 1       )
       quick_sort_apartition(SCREAMING_SNAKE_CASE__		,						b + 1		,						SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     None:
  
  
  
  
  
       '''simple docstring'''
  
       if left < right:
            _UpperCAmelCase    :			Optional[int]              =					lomuto_partition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
            quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						pivot_index - 1       )
            quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE__		,						pivot_index + 1		,						SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Dict              =					sorting[right]
       _UpperCAmelCase    :			Any              =					left
       for i in range(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
            if sorting[i] < pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					sorting[i], sorting[store_index]
                 store_index += 1
       _UpperCAmelCase       , _UpperCAmelCase    :			List[str]              =					sorting[store_index], sorting[right]
       return store_index
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     list:
  
  
  
  
  
       '''simple docstring'''
  
       if len(SCREAMING_SNAKE_CASE__       ) <= 1:
            return sorting
       return (
           three_way_radix_quicksort([i for i in sorting if i < sorting[0]]       )
           + [i for i in sorting if i == sorting[0]]
           + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]       )
       )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod(verbose=True)
  
   _lowerCAmelCase       :      Union[str, Any]							=							input("Enter numbers separated by a comma:\n").strip()
   _lowerCAmelCase       :      List[str]							=							[int(item) for item in user_input.split(",")]
   quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
   print(unsorted)
  