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 						lowerCAmelCase_		(      _snake_case   :						Union[str, Any]   ,     _snake_case   :						int     )       ->	Any:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	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    _snake_case      (  torch.optim.Optimizer       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a=True		,     _a=None	):
  				__magic_name__	:	Union[str, Any]			     =  optimizer
  				__magic_name__	:	List[str]			     =  scaler
  				__magic_name__	:	Tuple			     =  AcceleratorState()
  				__magic_name__	:	Tuple			     =  GradientState()
  				__magic_name__	:	Optional[int]			     =  device_placement
  				__magic_name__	:	int			     =  False
  				__magic_name__	:	Optional[int]			     =  None
  
  				# Handle device placement
  				if device_placement:
  					__magic_name__	:	Tuple			     =  self.optimizer.state_dict()
  					if self.accelerator_state.distributed_type == DistributedType.TPU:
  						xm.send_cpu_data_to_device(_a		,     self.accelerator_state.device	)
  					else:
  						__magic_name__	:	Tuple			     =  move_to_device(_a		,     self.accelerator_state.device	)
  					self.optimizer.load_state_dict(_a	)
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return self.optimizer.state
  
  
  
  
  
  
  
  			@state.setter
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	Union[str, Any]			     =  state
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return self.optimizer.param_groups
  
  
  
  
  
  
  
  			@param_groups.setter
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	Union[str, Any]			     =  param_groups
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return self.optimizer.defaults
  
  
  
  
  
  
  
  			@defaults.setter
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	Union[str, Any]			     =  defaults
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				self.optimizer.add_param_group(_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  
  
  
  
  
  
  				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	):
  				return self.optimizer.state_dict()
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a=None	):
  
  
  
  
  
  
  				if self.gradient_state.sync_gradients:
  					__magic_name__	:	Optional[int]			     =  "set_to_none" in inspect.signature(self.optimizer.zero_grad	).parameters
  					if accept_arg:
  						if set_to_none is None:
  							__magic_name__	:	Optional[Any]			     =  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	):
  
  
  
  
  
  
  				if self.gradient_state.sync_gradients:
  					if self.accelerator_state.distributed_type == DistributedType.TPU:
  						__magic_name__	:	Tuple			     =  {"closure": closure} if closure is not None else {}
  						xm.optimizer_step(self.optimizer		,     optimizer_args=_a	)
  					elif self.scaler is not None:
  						__magic_name__	:	Any			     =  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
  							__magic_name__	:	Optional[int]			     =  self.scaler.get_scale()
  							__magic_name__	:	Dict			     =  True
  						self.scaler.step(self.optimizer		,     _a	)
  						self.scaler.update()
  						__magic_name__	:	Tuple			     =  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.
  							__magic_name__	:	str			     =  scale_after < self._last_scale
  						__magic_name__	:	int			     =  scale_after
  
  					else:
  						self.optimizer.step(_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  
  
  
  
  
  
  				for param_group in self.optimizer.param_groups:
  					__magic_name__	:	Optional[Any]			     =  [parameters_map.get(_a		,     _a	) for p in param_group["params"]]
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				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	):
  				return self._is_overflow
  
  
  
  
  
  
  
  			def __getstate__(       self	):
  				return self.__dict__.copy()
  
  
  
  
  
  
  
  			def __setstate__(       self		,     _a	):
  				self.__dict__.update(_a	)
  
style_context: 
  def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	int:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if not isinstance(_snake_case   ,     _snake_case     ) or number < 0:
  		raise ValueError("Input must be a non-negative integer"     )
  
  	__magic_name__	:	Dict			     =  number & ~(number - 1)
  	__magic_name__	:	Optional[Any]			     =  0
  	while intermediate:
  		intermediate >>= 1
  		index += 1
  	return index - 1
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod(verbose=True)
  