



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





class 	__lowerCAmelCase       (		torch.optim.Optimizer				):




 def __init__(  self		,			__UpperCAmelCase		,			__UpperCAmelCase=True		,			__UpperCAmelCase=None			):



    '''simple docstring'''
    __lowerCamelCase  							=      optimizer
    __lowerCamelCase  							=      scaler
    __lowerCamelCase  							=      AcceleratorState()
    __lowerCamelCase  							=      GradientState()
    __lowerCamelCase  							=      device_placement
    __lowerCamelCase  							=      False
    __lowerCamelCase  							=      None

    # Handle device placement
    if device_placement:
       __lowerCamelCase  							=      self.optimizer.state_dict()
       if self.accelerator_state.distributed_type == DistributedType.TPU:
          xm.send_cpu_data_to_device(__UpperCAmelCase		,			self.accelerator_state.device			)
       else:
          __lowerCamelCase  							=      move_to_device(__UpperCAmelCase		,			self.accelerator_state.device			)
       self.optimizer.load_state_dict(__UpperCAmelCase			)




 @property
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    return self.optimizer.state




 @state.setter
 def     lowerCamelCase (  self		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      state




 @property
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    return self.optimizer.param_groups




 @param_groups.setter
 def     lowerCamelCase (  self		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      param_groups




 @property
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    return self.optimizer.defaults




 @defaults.setter
 def     lowerCamelCase (  self		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      defaults




 def     lowerCamelCase (  self		,			__UpperCAmelCase			):



    '''simple docstring'''
    self.optimizer.add_param_group(__UpperCAmelCase			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase			):



    '''simple docstring'''
    if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
       xm.send_cpu_data_to_device(__UpperCAmelCase		,			self.accelerator_state.device			)
    self.optimizer.load_state_dict(__UpperCAmelCase			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    return self.optimizer.state_dict()




 def     lowerCamelCase (  self		,			__UpperCAmelCase=None			):



    '''simple docstring'''
    if self.gradient_state.sync_gradients:
       __lowerCamelCase  							=      '''set_to_none''' in inspect.signature(self.optimizer.zero_grad			).parameters
       if accept_arg:
          if set_to_none is None:
             __lowerCamelCase  							=      False
          self.optimizer.zero_grad(set_to_none=__UpperCAmelCase			)
       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     lowerCamelCase (  self		,			__UpperCAmelCase=None			):



    '''simple docstring'''
    if self.gradient_state.sync_gradients:
       if self.accelerator_state.distributed_type == DistributedType.TPU:
          __lowerCamelCase  							=      {'''closure''': closure} if closure is not None else {}
          xm.optimizer_step(self.optimizer		,			optimizer_args=__UpperCAmelCase			)
       elif self.scaler is not None:
          __lowerCamelCase  							=      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
             __lowerCamelCase  							=      self.scaler.get_scale()
             __lowerCamelCase  							=      True
          self.scaler.step(self.optimizer		,			__UpperCAmelCase			)
          self.scaler.update()
          __lowerCamelCase  							=      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.
             __lowerCamelCase  							=      scale_after < self._last_scale
          __lowerCamelCase  							=      scale_after

       else:
          self.optimizer.step(__UpperCAmelCase			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase			):



    '''simple docstring'''
    for param_group in self.optimizer.param_groups:
       __lowerCamelCase  							=      [parameters_map.get(__UpperCAmelCase		,			__UpperCAmelCase			) for p in param_group['''params''']]




 @property
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    warnings.warn(
        '''The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '''
        '''`optimizer.step_was_skipped` instead.'''		,			__UpperCAmelCase		,			)
    return self._is_overflow




 @property
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    return self._is_overflow




 def __getstate__(  self			):



    '''simple docstring'''
    return self.__dict__.copy()




 def __setstate__(  self		,			__UpperCAmelCase			):



    '''simple docstring'''
    self.__dict__.update(__UpperCAmelCase			)











def 						a__     (						_UpperCamelCase     :	list	,_UpperCamelCase     :	list	,_UpperCamelCase     :	list	,_UpperCamelCase     :	int	,_UpperCamelCase     :	int							):
   if target == 0:
      answer.append(path.copy()							)
   else:
      for index in range(_UpperCamelCase	,len(_UpperCamelCase							)							):
         if target >= candidates[index]:
            path.append(candidates[index]							)
            backtrack(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,target - candidates[index]	,_UpperCamelCase							)
            path.pop(len(_UpperCamelCase							) - 1							)





def 						a__     (						_UpperCamelCase     :	list	,_UpperCamelCase     :	int							):
   __lowerCamelCase  							=      []  # type: list[int]
   __lowerCamelCase  							=      []  # type: list[int]
   backtrack(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,0							)
   return answer





def 						a__     (						):
   print(combination_sum([-8, 2.3, 0]	,1							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()






