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        __SCREAMING_SNAKE_CASE    (      a__						:      List[Any] ,a__						:      Optional[Any]					)				->							Union[str, Any]:
  	if isinstance(a__ ,(list, tuple)					):
  		return honor_type(a__ ,(move_to_device(a__ ,a__					) for t in state)					)
  	elif isinstance(a__ ,a__					):
  		return type(a__					)({k: move_to_device(a__ ,a__					) for k, v in state.items()}					)
  	elif isinstance(a__ ,torch.Tensor					):
  		return state.to(a__					)
  	return state
  
  
  
  
  
  
  
  class     lowerCamelCase_			(  torch.optim.Optimizer  ):
  
  
  
  
  	def __init__(    self	:    Any				,			__A	:    Any				,			__A	:    Optional[Any]=True				,			__A	:    Union[str, Any]=None       ):
  		__A   : Tuple								= optimizer
  		__A   : Dict								= scaler
  		__A   : Dict								= AcceleratorState()
  		__A   : Optional[Any]								= GradientState()
  		__A   : List[str]								= device_placement
  		__A   : Dict								= False
  		__A   : Union[str, Any]								= None
  
  		# Handle device placement
  		if device_placement:
  			__A   : Optional[Any]								= 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   : Union[str, Any]								= move_to_device(__A				,			self.accelerator_state.device       )
  			self.optimizer.load_state_dict(__A       )
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
  		return self.optimizer.state
  
  
  
  
  	@state.setter
  	def 					lowerCAmelCase_					(    self	:    Optional[Any]				,			__A	:    int       ):
  		__A   : List[str]								= state
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    List[str]       ):
  		return self.optimizer.param_groups
  
  
  
  
  	@param_groups.setter
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    Union[str, Any]       ):
  		__A   : str								= param_groups
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Tuple       ):
  		return self.optimizer.defaults
  
  
  
  
  	@defaults.setter
  	def 					lowerCAmelCase_					(    self	:    int				,			__A	:    Tuple       ):
  		__A   : Union[str, Any]								= defaults
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    str				,			__A	:    Optional[Any]       ):
  		self.optimizer.add_param_group(__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    str				,			__A	:    Dict       ):
  		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 					lowerCAmelCase_					(    self	:    Dict       ):
  		return self.optimizer.state_dict()
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    str				,			__A	:    List[str]=None       ):
  		if self.gradient_state.sync_gradients:
  			__A   : Tuple								= """set_to_none""" in inspect.signature(self.optimizer.zero_grad       ).parameters
  			if accept_arg:
  				if set_to_none is None:
  					__A   : Dict								= 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 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    str=None       ):
  		if self.gradient_state.sync_gradients:
  			if self.accelerator_state.distributed_type == DistributedType.TPU:
  				__A   : List[str]								= {"""closure""": closure} if closure is not None else {}
  				xm.optimizer_step(self.optimizer				,			optimizer_args=__A       )
  			elif self.scaler is not None:
  				__A   : Optional[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
  					__A   : List[str]								= self.scaler.get_scale()
  					__A   : str								= True
  				self.scaler.step(self.optimizer				,			__A       )
  				self.scaler.update()
  				__A   : List[str]								= 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   : List[Any]								= scale_after < self._last_scale
  				__A   : Optional[int]								= scale_after
  
  			else:
  				self.optimizer.step(__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    int				,			__A	:    Tuple       ):
  		for param_group in self.optimizer.param_groups:
  			__A   : Optional[Any]								= [parameters_map.get(__A				,			__A       ) for p in param_group["""params"""]]
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Dict       ):
  		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 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		return self._is_overflow
  
  
  
  
  	def __getstate__(    self	:    Union[str, Any]       ):
  		return self.__dict__.copy()
  
  
  
  
  	def __setstate__(    self	:    int				,			__A	:    int       ):
  		self.__dict__.update(__A       )
  
  
  
  
  
  
style_context: 
  
  UpperCAmelCase_       :				str		   =      [
      [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
      [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
      [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
      [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
  ]
  def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      int ,a__						:      int ,a__						:      int					)				->							bool:
  	return 0 <= row < rows and 0 <= col < cols
  def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      int ,a__						:      set ,a__						:      list[list[int]]					)				->							int:
  	__A   : Union[str, Any]								= len(a__					)
  	__A   : Any								= len(mat[0]					)
  	if is_safe(a__ ,a__ ,a__ ,a__					) and (row, col) not in seen and mat[row][col] == 1:
  		seen.add((row, col)					)
  		return (
  		    1
  		    + depth_first_search(row + 1 ,a__ ,a__ ,a__					)
  		    + depth_first_search(row - 1 ,a__ ,a__ ,a__					)
  		    + depth_first_search(a__ ,col + 1 ,a__ ,a__					)
  		    + depth_first_search(a__ ,col - 1 ,a__ ,a__					)
  		)
  	else:
  		return 0
  def        __SCREAMING_SNAKE_CASE    (      a__						:      list[list[int]]					)				->							int:
  	__A   : set								= set()
  
  	__A   : Union[str, Any]								= 0
  	for row, line in enumerate(a__					):
  		for col, item in enumerate(a__					):
  			if item == 1 and (row, col) not in seen:
  				# Maximizing the area
  				__A   : Optional[Any]								= max(a__ ,depth_first_search(a__ ,a__ ,a__ ,a__					)					)
  	return max_area
  
  
  if __name__ == "__main__":
  			import doctest
  
  			print(find_max_area(matrix))  # Output -> 6
  			doctest.testmod()
  
  
  
  
  
  