



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   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[Any]  ,      _snake_case  :List[Any]							)     ->    List[str]:
   if isinstance(_snake_case  ,      (list, tuple)							):
      return honor_type(_snake_case  ,      (move_to_device(_snake_case  ,      _snake_case							) for t in state)							)
   elif isinstance(_snake_case  ,      _snake_case							):
      return type(_snake_case							)({k: move_to_device(_snake_case  ,      _snake_case							) for k, v in state.items()}							)
   elif isinstance(_snake_case  ,      torch.Tensor							):
      return state.to(_snake_case							)
   return state




class   lowerCamelCase__  (					torch.optim.Optimizer):



 """simple docstring"""

 def __init__(						self				:				Any ,	__lowerCAmelCase				:				List[str] ,	__lowerCAmelCase				:				List[str]=True ,	__lowerCAmelCase				:				Dict=None			)      ->  Tuple:
    _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(lowerCamelCase__ ,	self.accelerator_state.device			)
       else:
          _A						     =					move_to_device(lowerCamelCase__ ,	self.accelerator_state.device			)
       self.optimizer.load_state_dict(lowerCamelCase__			)

 @property
 def 						snake_case_			(						self				:				Tuple			)      ->  Any:
    return self.optimizer.state

 @state.setter
 def 						snake_case_			(						self				:				str ,	__lowerCAmelCase				:				Dict			)      ->  Tuple:
    _A						     =					state

 @property
 def 						snake_case_			(						self				:				Optional[int]			)      ->  str:
    return self.optimizer.param_groups

 @param_groups.setter
 def 						snake_case_			(						self				:				str ,	__lowerCAmelCase				:				Dict			)      ->  Optional[Any]:
    _A						     =					param_groups

 @property
 def 						snake_case_			(						self				:				Dict			)      ->  List[Any]:
    return self.optimizer.defaults

 @defaults.setter
 def 						snake_case_			(						self				:				Tuple ,	__lowerCAmelCase				:				List[Any]			)      ->  Any:
    _A						     =					defaults

 def 						snake_case_			(						self				:				Any ,	__lowerCAmelCase				:				List[Any]			)      ->  Optional[int]:
    self.optimizer.add_param_group(lowerCamelCase__			)

 def 						snake_case_			(						self				:				Union[str, Any] ,	__lowerCAmelCase				:				List[Any]			)      ->  str:
    if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
       xm.send_cpu_data_to_device(lowerCamelCase__ ,	self.accelerator_state.device			)
    self.optimizer.load_state_dict(lowerCamelCase__			)

 def 						snake_case_			(						self				:				List[str]			)      ->  int:
    return self.optimizer.state_dict()

 def 						snake_case_			(						self				:				List[Any] ,	__lowerCAmelCase				:				Any=None			)      ->  Optional[int]:
    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=lowerCamelCase__			)
       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 						snake_case_			(						self				:				Tuple ,	__lowerCAmelCase				:				Any=None			)      ->  Optional[Any]:
    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=lowerCamelCase__			)
       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 ,	lowerCamelCase__			)
          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(lowerCamelCase__			)

 def 						snake_case_			(						self				:				Dict ,	__lowerCAmelCase				:				Dict			)      ->  Optional[int]:
    for param_group in self.optimizer.param_groups:
       _A						     =					[parameters_map.get(lowerCamelCase__ ,	lowerCamelCase__			) for p in param_group["params"]]

 @property
 def 						snake_case_			(						self				:				Tuple			)      ->  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.''' ,	lowerCamelCase__ ,	)
    return self._is_overflow

 @property
 def 						snake_case_			(						self				:				Optional[Any]			)      ->  Dict:
    return self._is_overflow

 def __getstate__(						self				:				List[str]			)      ->  List[Any]:
    return self.__dict__.copy()

 def __setstate__(						self				:				List[Any] ,	__lowerCAmelCase				:				str			)      ->  int:
    self.__dict__.update(lowerCamelCase__			)








def   SCREAMING_SNAKE_CASE_       (  _snake_case  :list							)     ->    list:
   if len(_snake_case							) <= 1:
      return data
   else:
      return [
          *quick_sort([e for e in data[1:] if e <= data[0]]							),
          data[0],
          *quick_sort([e for e in data[1:] if e > data[0]]							),
      ]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



