code: 
  '''simple docstring'''
  
  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 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      List[str]:
  		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 			UpperCAmelCase				(					torch.optim.Optimizer ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def __init__(    self  ,					_snake_case  ,					_snake_case=True  ,					_snake_case=None     )				->    Tuple:
  				_UpperCamelCase       : List[str]    =      optimizer
  				_UpperCamelCase       : Any    =      scaler
  				_UpperCamelCase       : Optional[Any]    =      AcceleratorState()
  				_UpperCamelCase       : int    =      GradientState()
  				_UpperCamelCase       : Dict    =      device_placement
  				_UpperCamelCase       : str    =      False
  				_UpperCamelCase       : List[Any]    =      None
  
  				# Handle device placement
  				if device_placement:
  						_UpperCamelCase       : Any    =      self.optimizer.state_dict()
  						if self.accelerator_state.distributed_type == DistributedType.TPU:
  								xm.send_cpu_data_to_device(SCREAMING_SNAKE_CASE_  ,					self.accelerator_state.device     )
  						else:
  								_UpperCamelCase       : Any    =      move_to_device(SCREAMING_SNAKE_CASE_  ,					self.accelerator_state.device     )
  						self.optimizer.load_state_dict(SCREAMING_SNAKE_CASE_     )
  		@property
  		def _lowercase  (    self     )				->    Tuple:
  				return self.optimizer.state
  		@state.setter
  		def _lowercase  (    self  ,					_snake_case     )				->    Tuple:
  				_UpperCamelCase       : Optional[int]    =      state
  		@property
  		def _lowercase  (    self     )				->    Any:
  				return self.optimizer.param_groups
  		@param_groups.setter
  		def _lowercase  (    self  ,					_snake_case     )				->    Optional[int]:
  				_UpperCamelCase       : Tuple    =      param_groups
  		@property
  		def _lowercase  (    self     )				->    List[str]:
  				return self.optimizer.defaults
  		@defaults.setter
  		def _lowercase  (    self  ,					_snake_case     )				->    Tuple:
  				_UpperCamelCase       : Optional[int]    =      defaults
  		def _lowercase  (    self  ,					_snake_case     )				->    Dict:
  				self.optimizer.add_param_group(SCREAMING_SNAKE_CASE_     )
  		def _lowercase  (    self  ,					_snake_case     )				->    Tuple:
  				if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
  						xm.send_cpu_data_to_device(SCREAMING_SNAKE_CASE_  ,					self.accelerator_state.device     )
  				self.optimizer.load_state_dict(SCREAMING_SNAKE_CASE_     )
  		def _lowercase  (    self     )				->    Any:
  				return self.optimizer.state_dict()
  		def _lowercase  (    self  ,					_snake_case=None     )				->    Any:
  				if self.gradient_state.sync_gradients:
  						_UpperCamelCase       : Any    =      '''set_to_none''' in inspect.signature(self.optimizer.zero_grad     ).parameters
  						if accept_arg:
  								if set_to_none is None:
  										_UpperCamelCase       : List[Any]    =      False
  								self.optimizer.zero_grad(set_to_none=SCREAMING_SNAKE_CASE_     )
  						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 _lowercase  (    self  ,					_snake_case=None     )				->    int:
  				if self.gradient_state.sync_gradients:
  						if self.accelerator_state.distributed_type == DistributedType.TPU:
  								_UpperCamelCase       : int    =      {'''closure''': closure} if closure is not None else {}
  								xm.optimizer_step(self.optimizer  ,					optimizer_args=SCREAMING_SNAKE_CASE_     )
  						elif self.scaler is not None:
  								_UpperCamelCase       : str    =      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
  										_UpperCamelCase       : Union[str, Any]    =      self.scaler.get_scale()
  										_UpperCamelCase       : Any    =      True
  								self.scaler.step(self.optimizer  ,					SCREAMING_SNAKE_CASE_     )
  								self.scaler.update()
  								_UpperCamelCase       : int    =      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.
  										_UpperCamelCase       : List[str]    =      scale_after < self._last_scale
  								_UpperCamelCase       : List[str]    =      scale_after
  
  						else:
  								self.optimizer.step(SCREAMING_SNAKE_CASE_     )
  		def _lowercase  (    self  ,					_snake_case     )				->    Dict:
  				for param_group in self.optimizer.param_groups:
  						_UpperCamelCase       : Optional[Any]    =      [parameters_map.get(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_     ) for p in param_group['''params''']]
  		@property
  		def _lowercase  (    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.'''  ,					SCREAMING_SNAKE_CASE_  ,					)
  				return self._is_overflow
  		@property
  		def _lowercase  (    self     )				->    Any:
  				return self._is_overflow
  		def __getstate__(    self     )				->    str:
  				return self.__dict__.copy()
  
  
  
  
  		def __setstate__(    self  ,					_snake_case     )				->    Any:
  				self.__dict__.update(SCREAMING_SNAKE_CASE_     )
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  from __future__ import annotations
  
  from collections.abc import Iterable
  from dataclasses import dataclass
  from typing import Any
  
  class 			UpperCAmelCase				:
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   List[str]          =    ('_front', '_back', '_len')
  
  		@dataclass
  		class 			UpperCAmelCase				:
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  				A__  :   Any          =    None
  				A__  :   Deque._Node | None          =    None
  				A__  :   Deque._Node | None          =    None
  
  
  
  
  
  
  
  		class 			UpperCAmelCase				:
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  				A__  :   Any          =    ('_cur',)
  				def __init__(    self  ,					_snake_case     )				->    None:
  						_UpperCamelCase       : Any    =      cur
  				def __iter__(    self     )				->    Deque._Iterator:
  						return self
  
  
  
  
  				def _lowercase  (    self     )				->    Any:
  						if self._cur is None:
  								# finished iterating
  								raise StopIteration
  						_UpperCamelCase       : List[Any]    =      self._cur.val
  						_UpperCamelCase       : List[Any]    =      self._cur.next_node
  
  						return val
  
  
  
  
  
  
  
  		def __init__(    self  ,					_snake_case = None     )				->    None:
  				_UpperCamelCase       : Any    =      None
  				_UpperCamelCase       : Any    =      None
  				_UpperCamelCase       : int    =      0
  
  				if iterable is not None:
  						# append every value to the deque
  						for val in iterable:
  								self.append(_snake_case     )
  		def _lowercase  (    self  ,					_snake_case     )				->    None:
  				_UpperCamelCase       : List[str]    =      self._Node(_snake_case  ,					_snake_case  ,					_snake_case     )
  				if self.is_empty():
  						# front = back
  						_UpperCamelCase       : Union[str, Any]    =      node
  						_UpperCamelCase       : Tuple    =      1
  				else:
  						# connect nodes
  						_UpperCamelCase       : Union[str, Any]    =      node
  						_UpperCamelCase       : int    =      self._back
  						_UpperCamelCase       : List[Any]    =      node  # assign new back to the new node
  
  						self._len += 1
  
  						# make sure there were no errors
  						assert not self.is_empty(), "Error on appending value."
  		def _lowercase  (    self  ,					_snake_case     )				->    None:
  				_UpperCamelCase       : Optional[Any]    =      self._Node(_snake_case  ,					_snake_case  ,					_snake_case     )
  				if self.is_empty():
  						# front = back
  						_UpperCamelCase       : Union[str, Any]    =      node
  						_UpperCamelCase       : Optional[int]    =      1
  				else:
  						# connect nodes
  						_UpperCamelCase       : Tuple    =      self._front
  						_UpperCamelCase       : Optional[int]    =      node
  						_UpperCamelCase       : List[str]    =      node  # assign new front to the new node
  
  						self._len += 1
  
  						# make sure there were no errors
  						assert not self.is_empty(), "Error on appending value."
  		def _lowercase  (    self  ,					_snake_case     )				->    None:
  				for val in iterable:
  						self.append(_snake_case     )
  		def _lowercase  (    self  ,					_snake_case     )				->    None:
  				for val in iterable:
  						self.appendleft(_snake_case     )
  		def _lowercase  (    self     )				->    Any:
  				assert not self.is_empty(), "Deque is empty."
  
  				_UpperCamelCase       : int    =      self._back
  				_UpperCamelCase       : List[Any]    =      self._back.prev_node  # set new back
  				# drop the last node - python will deallocate memory automatically
  				_UpperCamelCase       : Union[str, Any]    =      None
  
  				self._len -= 1
  
  				return topop.val
  		def _lowercase  (    self     )				->    Any:
  				assert not self.is_empty(), "Deque is empty."
  
  				_UpperCamelCase       : List[Any]    =      self._front
  				_UpperCamelCase       : str    =      self._front.next_node  # set new front and drop the first node
  				_UpperCamelCase       : Union[str, Any]    =      None
  
  				self._len -= 1
  
  				return topop.val
  		def _lowercase  (    self     )				->    bool:
  				return self._front is None
  		def __len__(    self     )				->    int:
  				return self._len
  		def __eq__(    self  ,					_snake_case     )				->    bool:
  
  				if not isinstance(_snake_case  ,					_snake_case     ):
  						return NotImplemented
  
  				_UpperCamelCase       : Optional[int]    =      self._front
  				_UpperCamelCase       : List[Any]    =      other._front
  
  				# if the length of the dequeues are not the same, they are not equal
  				if len(self     ) != len(_snake_case     ):
  						return False
  
  				while me is not None and oth is not None:
  						# compare every value
  						if me.val != oth.val:
  								return False
  						_UpperCamelCase       : str    =      me.next_node
  						_UpperCamelCase       : List[str]    =      oth.next_node
  
  				return True
  		def __iter__(    self     )				->    Deque._Iterator:
  				return Deque._Iterator(self._front     )
  
  
  
  
  		def __repr__(    self     )				->    str:
  				_UpperCamelCase       : str    =      []
  				_UpperCamelCase       : Tuple    =      self._front
  				while aux is not None:
  						# append the values in a list to display
  						values_list.append(aux.val     )
  						_UpperCamelCase       : List[str]    =      aux.next_node
  
  				return F'''[{', '.join(repr(_snake_case     ) for val in values_list     )}]'''
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  