code: 
  
  
  import inspect
  import warnings
  
  import torch
  
  from .state import AcceleratorState, GradientState
  from .utils import DistributedType, honor_type, is_tpu_available
  
  
  if is_tpu_available(check_device=False):
         import torch_xla.core.xla_model as xm
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      if isinstance(lowercase ,					(list, tuple)	):
          return honor_type(lowercase ,					(move_to_device(lowercase ,					lowercase	) for t in state)	)
      elif isinstance(lowercase ,					lowercase	):
          return type(lowercase	)({k: move_to_device(lowercase ,					lowercase	) for k, v in state.items()}	)
      elif isinstance(lowercase ,					torch.Tensor	):
          return state.to(lowercase	)
      return state
  
  
  
  
  
  
  class    __A       ( torch.optim.Optimizer    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A				,					__A=True				,					__A=None		)      ->  Union[str, Any]:
       a					=optimizer
       a					=scaler
       a					=AcceleratorState()
       a					=GradientState()
       a					=device_placement
       a					=False
       a					=None
  
       # Handle device placement
       if device_placement:
           a					=self.optimizer.state_dict()
           if self.accelerator_state.distributed_type == DistributedType.TPU:
               xm.send_cpu_data_to_device(__A				,					self.accelerator_state.device		)
           else:
               a					=move_to_device(__A				,					self.accelerator_state.device		)
           self.optimizer.load_state_dict(__A		)
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       return self.optimizer.state
  
  
   @state.setter
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Optional[Any]:
       a					=state
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       return self.optimizer.param_groups
  
  
   @param_groups.setter
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Dict:
       a					=param_groups
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       return self.optimizer.defaults
  
  
   @defaults.setter
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Union[str, Any]:
       a					=defaults
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  str:
       self.optimizer.add_param_group(__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Tuple:
  
  
  
  
  
  
  
       if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
           xm.send_cpu_data_to_device(__A				,					self.accelerator_state.device		)
       self.optimizer.load_state_dict(__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
       return self.optimizer.state_dict()
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A=None		)      ->  List[Any]:
  
  
  
  
  
  
  
       if self.gradient_state.sync_gradients:
           a					='''set_to_none''' in inspect.signature(self.optimizer.zero_grad		).parameters
           if accept_arg:
               if set_to_none is None:
                   a					=False
               self.optimizer.zero_grad(set_to_none=__A		)
           else:
               if set_to_none is not None:
                   raise ValueError('''`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'''		)
               self.optimizer.zero_grad()
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A=None		)      ->  str:
  
  
  
  
  
  
  
       if self.gradient_state.sync_gradients:
           if self.accelerator_state.distributed_type == DistributedType.TPU:
               a					={'''closure''': closure} if closure is not None else {}
               xm.optimizer_step(self.optimizer				,					optimizer_args=__A		)
           elif self.scaler is not None:
               a					=False
               if self._last_scale is None:
                   # `get_scale` is an async operation requiring full synchronization
                   # on CPU and GPUs before finishing. As a result, we store away
                   # the prior one to reduce the call overhead
                   a					=self.scaler.get_scale()
                   a					=True
               self.scaler.step(self.optimizer				,					__A		)
               self.scaler.update()
               a					=self.scaler.get_scale()
               if not new_scale:
                   # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
                   a					=scale_after < self._last_scale
               a					=scale_after
  
           else:
               self.optimizer.step(__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  List[Any]:
  
  
  
  
  
  
  
       for param_group in self.optimizer.param_groups:
           a					=[parameters_map.get(__A				,					__A		) for p in param_group['''params''']]
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
       warnings.warn(
           '''The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '''
           '''`optimizer.step_was_skipped` instead.'''				,					__A				,					)
       return self._is_overflow
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
       return self._is_overflow
  
  
   def __getstate__(	self		)      ->  Union[str, Any]:
       return self.__dict__.copy()
  
  
  
  
  
   def __setstate__(	self				,					__A		)      ->  List[Any]:
       self.__dict__.update(__A		)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from random import randrange
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if len(lowercase	) < 2:
          return collection
      a					=randrange(len(lowercase	)	)  # Use random element as pivot
      a					=collection[pivot_index]
      a					=[]  # All elements greater than pivot
      a					=[]  # All elements less than or equal to pivot
  
      for element in collection[:pivot_index]:
          (greater if element > pivot else lesser).append(lowercase	)
  
      for element in collection[pivot_index + 1 :]:
          (greater if element > pivot else lesser).append(lowercase	)
  
      return [*quick_sort(lowercase	), pivot, *quick_sort(lowercase	)]
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Union[str, Any]    		=						input("""Enter numbers separated by a comma:\n""").strip()
         lowerCamelCase_     :     Union[str, Any]    		=						[int(item) for item in user_input.split(""",""")]
         print(quick_sort(unsorted))