code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import tempfile
  
  import torch
  
  from diffusers import (
      DEISMultistepScheduler,
      DPMSolverMultistepScheduler,
      DPMSolverSinglestepScheduler,
      UniPCMultistepScheduler,
  )
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     Union[str, Any]				  =				(DPMSolverMultistepScheduler,)
        __SCREAMING_SNAKE_CASE		:     Optional[Any]				  =				(('num_inference_steps', 2_5),)
  
  
  
        def 		snake_case_      (  self  :			Optional[int]  ,			**A  :			Tuple		):
             _UpperCAmelCase    :			str              =					{
                 "num_train_timesteps": 1_0_0_0,
                 "beta_start": 0.0_001,
                 "beta_end": 0.02,
                 "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(**A		)
             return config
  
  
  
        def 		snake_case_      (  self  :			Tuple  ,			A  :			int=0  ,			**A  :			Tuple		):
             _UpperCAmelCase    :			List[Any]              =					dict(self.forward_default_kwargs		)
             _UpperCAmelCase    :			Tuple              =					kwargs.pop("num_inference_steps"  ,			A		)
             _UpperCAmelCase    :			Union[str, Any]              =					self.dummy_sample
             _UpperCAmelCase    :			Union[str, Any]              =					0.1 * sample
             _UpperCAmelCase    :			Tuple              =					[residual + 0.2, residual + 0.15, residual + 0.10]
  
             for scheduler_class in self.scheduler_classes:
                  _UpperCAmelCase    :			Dict              =					self.get_scheduler_config(**A		)
                  _UpperCAmelCase    :			int              =					scheduler_class(**A		)
                  scheduler.set_timesteps(A		)
                  # copy over dummy past residuals
                  _UpperCAmelCase    :			str              =					dummy_past_residuals[: scheduler.config.solver_order]
  
                  with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(A		)
                       _UpperCAmelCase    :			Tuple              =					scheduler_class.from_pretrained(A		)
                       new_scheduler.set_timesteps(A		)
                       # copy over dummy past residuals
                       _UpperCAmelCase    :			Tuple              =					dummy_past_residuals[: new_scheduler.config.solver_order]
  
                  _UpperCAmelCase    :			Tuple              =					sample, sample
                  for t in range(A  ,			time_step + scheduler.config.solver_order + 1		):
                       _UpperCAmelCase    :			int              =					scheduler.step(A  ,			A  ,			A  ,			**A		).prev_sample
                       _UpperCAmelCase    :			str              =					new_scheduler.step(A  ,			A  ,			A  ,			**A		).prev_sample
  
                       assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]		):
             pass
  
  
  
        def 		snake_case_      (  self  :			Tuple  ,			A  :			str=0  ,			**A  :			Optional[Any]		):
             _UpperCAmelCase    :			Any              =					dict(self.forward_default_kwargs		)
             _UpperCAmelCase    :			Optional[Any]              =					kwargs.pop("num_inference_steps"  ,			A		)
             _UpperCAmelCase    :			str              =					self.dummy_sample
             _UpperCAmelCase    :			List[Any]              =					0.1 * sample
             _UpperCAmelCase    :			Optional[int]              =					[residual + 0.2, residual + 0.15, residual + 0.10]
  
             for scheduler_class in self.scheduler_classes:
                  _UpperCAmelCase    :			Optional[Any]              =					self.get_scheduler_config()
                  _UpperCAmelCase    :			Optional[Any]              =					scheduler_class(**A		)
                  scheduler.set_timesteps(A		)
  
                  # copy over dummy past residuals (must be after setting timesteps)
                  _UpperCAmelCase    :			Any              =					dummy_past_residuals[: scheduler.config.solver_order]
  
                  with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(A		)
                       _UpperCAmelCase    :			Any              =					scheduler_class.from_pretrained(A		)
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(A		)
  
                       # copy over dummy past residual (must be after setting timesteps)
                       _UpperCAmelCase    :			Any              =					dummy_past_residuals[: new_scheduler.config.solver_order]
  
                  _UpperCAmelCase    :			Optional[Any]              =					scheduler.step(A  ,			A  ,			A  ,			**A		).prev_sample
                  _UpperCAmelCase    :			List[Any]              =					new_scheduler.step(A  ,			A  ,			A  ,			**A		).prev_sample
  
                  assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
  
  
  
        def 		snake_case_      (  self  :			str  ,			A  :			int=None  ,			**A  :			Union[str, Any]		):
             if scheduler is None:
                  _UpperCAmelCase    :			Optional[int]              =					self.scheduler_classes[0]
                  _UpperCAmelCase    :			List[Any]              =					self.get_scheduler_config(**A		)
                  _UpperCAmelCase    :			str              =					scheduler_class(**A		)
  
             _UpperCAmelCase    :			List[Any]              =					1_0
             _UpperCAmelCase    :			Union[str, Any]              =					self.dummy_model()
             _UpperCAmelCase    :			List[str]              =					self.dummy_sample_deter
             scheduler.set_timesteps(A		)
  
             for i, t in enumerate(scheduler.timesteps		):
                  _UpperCAmelCase    :			Any              =					model(A  ,			A		)
                  _UpperCAmelCase    :			List[str]              =					scheduler.step(A  ,			A  ,			A		).prev_sample
  
             return sample
  
  
  
        def 		snake_case_      (  self  :			str		):
             _UpperCAmelCase    :			Optional[Any]              =					dict(self.forward_default_kwargs		)
  
             _UpperCAmelCase    :			str              =					kwargs.pop("num_inference_steps"  ,			A		)
  
             for scheduler_class in self.scheduler_classes:
                  _UpperCAmelCase    :			int              =					self.get_scheduler_config()
                  _UpperCAmelCase    :			Any              =					scheduler_class(**A		)
  
                  _UpperCAmelCase    :			List[str]              =					self.dummy_sample
                  _UpperCAmelCase    :			int              =					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    :			Union[str, Any]              =					num_inference_steps
  
                  # copy over dummy past residuals (must be done after set_timesteps)
                  _UpperCAmelCase    :			Optional[Any]              =					[residual + 0.2, residual + 0.15, residual + 0.10]
                  _UpperCAmelCase    :			str              =					dummy_past_residuals[: scheduler.config.solver_order]
  
                  _UpperCAmelCase    :			Optional[Any]              =					scheduler.timesteps[5]
                  _UpperCAmelCase    :			str              =					scheduler.timesteps[6]
  
                  _UpperCAmelCase    :			Any              =					scheduler.step(A  ,			A  ,			A  ,			**A		).prev_sample
                  _UpperCAmelCase    :			Dict              =					scheduler.step(A  ,			A  ,			A  ,			**A		).prev_sample
  
                  self.assertEqual(output_a.shape  ,			sample.shape		)
                  self.assertEqual(output_a.shape  ,			output_a.shape		)
  
  
  
        def 		snake_case_      (  self  :			Any		):
             for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                  self.check_over_configs(num_train_timesteps=A		)
  
  
  
        def 		snake_case_      (  self  :			Union[str, Any]		):
             self.check_over_configs(thresholding=A		)
             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=A  ,			prediction_type=A  ,			sample_max_value=A  ,			algorithm_type="dpmsolver++"  ,			solver_order=A  ,			solver_type=A  ,			)
  
  
  
        def 		snake_case_      (  self  :			Any		):
             for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=A		)
  
  
  
        def 		snake_case_      (  self  :			List[Any]		):
             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=A  ,			solver_type=A  ,			prediction_type=A  ,			algorithm_type=A  ,			)
                                 _UpperCAmelCase    :			Union[str, Any]              =					self.full_loop(
                                     solver_order=A  ,			solver_type=A  ,			prediction_type=A  ,			algorithm_type=A  ,			)
                                 assert not torch.isnan(A		).any(), "Samples have nan numbers"
  
  
  
        def 		snake_case_      (  self  :			int		):
             self.check_over_configs(lower_order_final=A		)
             self.check_over_configs(lower_order_final=A		)
  
  
  
        def 		snake_case_      (  self  :			Union[str, Any]		):
             self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
             self.check_over_configs(lambda_min_clipped=-5.1		)
  
  
  
        def 		snake_case_      (  self  :			str		):
             self.check_over_configs(variance_type=A		)
             self.check_over_configs(variance_type="learned_range"		)
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]		):
             for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                  self.check_over_forward(num_inference_steps=A  ,			time_step=0		)
  
  
  
        def 		snake_case_      (  self  :			Tuple		):
             _UpperCAmelCase    :			List[Any]              =					self.full_loop()
             _UpperCAmelCase    :			Any              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_mean.item() - 0.3_301		) < 1e-3
  
  
  
        def 		snake_case_      (  self  :			Dict		):
             _UpperCAmelCase    :			List[str]              =					self.full_loop(thresholding=A  ,			dynamic_thresholding_ratio=0.87  ,			sample_max_value=0.5		)
             _UpperCAmelCase    :			List[Any]              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_mean.item() - 1.1_364		) < 1e-3
  
  
  
        def 		snake_case_      (  self  :			Optional[int]		):
             _UpperCAmelCase    :			Optional[Any]              =					self.full_loop(prediction_type="v_prediction"		)
             _UpperCAmelCase    :			Any              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_mean.item() - 0.2_251		) < 1e-3
  
  
  
        def 		snake_case_      (  self  :			Tuple		):
             _UpperCAmelCase    :			str              =					self.full_loop(prediction_type="v_prediction"  ,			use_karras_sigmas=A		)
             _UpperCAmelCase    :			Optional[Any]              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_mean.item() - 0.2_096		) < 1e-3
  
  
  
        def 		snake_case_      (  self  :			List[str]		):
             # make sure that iterating over schedulers with same config names gives same results
             # for defaults
             _UpperCAmelCase    :			Union[str, Any]              =					DPMSolverMultistepScheduler(**self.get_scheduler_config()		)
             _UpperCAmelCase    :			Optional[Any]              =					self.full_loop(scheduler=A		)
             _UpperCAmelCase    :			int              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_mean.item() - 0.3_301		) < 1e-3
  
             _UpperCAmelCase    :			Union[str, Any]              =					DPMSolverSinglestepScheduler.from_config(scheduler.config		)
             _UpperCAmelCase    :			Dict              =					UniPCMultistepScheduler.from_config(scheduler.config		)
             _UpperCAmelCase    :			List[Any]              =					DEISMultistepScheduler.from_config(scheduler.config		)
             _UpperCAmelCase    :			List[Any]              =					DPMSolverMultistepScheduler.from_config(scheduler.config		)
  
             _UpperCAmelCase    :			Any              =					self.full_loop(scheduler=A		)
             _UpperCAmelCase    :			str              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_mean.item() - 0.3_301		) < 1e-3
  
  
  
        def 		snake_case_      (  self  :			str		):
             _UpperCAmelCase    :			Union[str, Any]              =					self.scheduler_classes[0]
             _UpperCAmelCase    :			Optional[int]              =					self.get_scheduler_config(thresholding=A  ,			dynamic_thresholding_ratio=0		)
             _UpperCAmelCase    :			List[Any]              =					scheduler_class(**A		)
  
             _UpperCAmelCase    :			int              =					1_0
             _UpperCAmelCase    :			Tuple              =					self.dummy_model()
             _UpperCAmelCase    :			List[Any]              =					self.dummy_sample_deter.half()
             scheduler.set_timesteps(A		)
  
             for i, t in enumerate(scheduler.timesteps		):
                  _UpperCAmelCase    :			Union[str, Any]              =					model(A  ,			A		)
                  _UpperCAmelCase    :			Tuple              =					scheduler.step(A  ,			A  ,			A		).prev_sample
  
             assert sample.dtype == torch.floataa
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]  ,			**A  :			str		):
             for scheduler_class in self.scheduler_classes:
                  _UpperCAmelCase    :			Any              =					self.get_scheduler_config(**A		)
                  _UpperCAmelCase    :			Union[str, Any]              =					scheduler_class(**A		)
  
                  scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
                  assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps
  
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)
  