


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		)



"""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))