code: 
  
  import random
  from typing import List, Optional, Union
  
  import numpy as np
  import torch
  
  from ..state import AcceleratorState
  from .constants import CUDA_DISTRIBUTED_TYPES
  from .dataclasses import DistributedType, RNGType
  from .imports import is_npu_available, is_tpu_available, is_xpu_available
  
  
  if is_tpu_available(check_device=False):
  			import torch_xla.core.xla_model as xm
  def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      bool = False					)				->							List[Any]:
  	if device_specific:
  		seed += AcceleratorState().process_index
  	random.seed(a__					)
  	np.random.seed(a__					)
  	torch.manual_seed(a__					)
  	if is_xpu_available():
  		torch.xpu.manual_seed_all(a__					)
  	elif is_npu_available():
  		torch.npu.manual_seed_all(a__					)
  	else:
  		torch.cuda.manual_seed_all(a__					)
  	# ^^ safe to call this function even if cuda is not available
  	if is_tpu_available():
  		xm.set_rng_state(a__					)
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[RNGType] = None ,a__						:      Optional[torch.Generator] = None					)				->							str:
  	# Get the proper rng state
  	if rng_type == RNGType.TORCH:
  		__A   : Tuple								= torch.get_rng_state()
  	elif rng_type == RNGType.CUDA:
  		__A   : Union[str, Any]								= torch.cuda.get_rng_state()
  	elif rng_type == RNGType.XLA:
  		assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
  		__A   : str								= torch.tensor(xm.get_rng_state()					)
  	elif rng_type == RNGType.NPU:
  		assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
  		__A   : Any								= torch.npu.get_rng_state()
  	elif rng_type == RNGType.XPU:
  		assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
  		__A   : List[str]								= torch.xpu.get_rng_state()
  	elif rng_type == RNGType.GENERATOR:
  		assert generator is not None, "Need a generator to synchronize its seed."
  		__A   : List[str]								= generator.get_state()
  
  	# Broadcast the rng state from device 0 to other devices
  	__A   : Union[str, Any]								= AcceleratorState()
  	if state.distributed_type == DistributedType.TPU:
  		__A   : int								= rng_state.to(xm.xla_device()					)
  		xm.collective_broadcast([rng_state]					)
  		xm.mark_step()
  		__A   : Dict								= rng_state.cpu()
  	elif (
  	    state.distributed_type in CUDA_DISTRIBUTED_TYPES
  	    or state.distributed_type == DistributedType.MULTI_NPU
  	    or state.distributed_type == DistributedType.MULTI_XPU
  	):
  		__A   : str								= rng_state.to(state.device					)
  		torch.distributed.broadcast(a__ ,0					)
  		__A   : Optional[int]								= rng_state.cpu()
  	elif state.distributed_type == DistributedType.MULTI_CPU:
  		torch.distributed.broadcast(a__ ,0					)
  
  	# Set the broadcast rng state
  	if rng_type == RNGType.TORCH:
  		torch.set_rng_state(a__					)
  	elif rng_type == RNGType.CUDA:
  		torch.cuda.set_rng_state(a__					)
  	elif rng_type == RNGType.NPU:
  		torch.npu.set_rng_state(a__					)
  	elif rng_type == RNGType.XPU:
  		torch.xpu.set_rng_state(a__					)
  	elif rng_type == RNGType.XLA:
  		xm.set_rng_state(rng_state.item()					)
  	elif rng_type == RNGType.GENERATOR:
  		generator.set_state(a__					)
  
  
  
  
  
  
  
  def        __SCREAMING_SNAKE_CASE    (      a__						:      List[Union[str, RNGType]] ,a__						:      Optional[torch.Generator] = None					)				->							Optional[int]:
  	for rng_type in rng_types:
  		synchronize_rng_state(RNGType(a__					) ,generator=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()
  
  
  
  
  
  