

'''simple docstring'''






# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')






class 			lowercase_	:




  """simple docstring"""




  def __init__(      self   :     List[str]					,lowercase__   :     Tuple					,lowercase__   :     Optional[int]					,lowercase__   :     bool = True					,lowercase__   :     bool = False			):
   __lowercase							  = scheduler
   __lowercase							  = optimizers if isinstance(lowercase__					,(list, tuple)			) else [optimizers]
   __lowercase							  = split_batches
   __lowercase							  = step_with_optimizer
   __lowercase							  = GradientState()






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,*lowercase__   :     str					,**lowercase__   :     str			):
   if not self.step_with_optimizer:
    # No link between scheduler and optimizer -> just step
    self.scheduler.step(*lowercase__					,**lowercase__			)
    return

   # Otherwise, first make sure the optimizer was stepped.
   if not self.gradient_state.sync_gradients:
    if self.gradient_state.adjust_scheduler:
     self.scheduler._step_count += 1
    return

   for opt in self.optimizers:
    if opt.step_was_skipped:
     return
   if self.split_batches:
    # Split batches -> the training dataloader batch size is not changed so one step per training step
    self.scheduler.step(*lowercase__					,**lowercase__			)
   else:
    # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
    # num_processes steps per training step
    __lowercase							  = AcceleratorState().num_processes
    for _ in range(lowercase__			):
     # Special case when using OneCycle and `drop_last` was not used
     if hasattr(self.scheduler					,'''total_steps'''			):
      if self.scheduler._step_count <= self.scheduler.total_steps:
       self.scheduler.step(*lowercase__					,**lowercase__			)
     else:
      self.scheduler.step(*lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   return self.scheduler.get_last_lr()






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   return self.scheduler.state_dict()






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     Tuple			):
   self.scheduler.load_state_dict(lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   return self.scheduler.get_lr()






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,*lowercase__   :     List[Any]					,**lowercase__   :     List[str]			):
   return self.scheduler.print_lr(*lowercase__					,**lowercase__			)




'''simple docstring'''






from __future__ import annotations


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = len(A__      )
 # We need to create solution object to save path.
 __lowercase							  = [[0 for _ in range(A__      )] for _ in range(A__      )]
 __lowercase							  = run_maze(A__     ,				0     ,				0     ,				A__      )
 if solved:
  print('''\n'''.join(str(A__      ) for row in solutions      )      )
 else:
  print('''No solution exists!'''      )
 return solved


def 		_A     (    A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = len(A__      )
 # Final check point.
 if i == j == (size - 1):
  __lowercase							  = 1
  return True

 __lowercase							  = (not i < 0) and (not j < 0)  # Check lower bounds
 __lowercase							  = (i < size) and (j < size)  # Check upper bounds

 if lower_flag and upper_flag:
  # check for already visited and block points.
  __lowercase							  = (not solutions[i][j]) and (not maze[i][j])
  if block_flag:
   # check visited
   __lowercase							  = 1

   # check for directions
   if (
       run_maze(A__     ,				i + 1     ,				A__     ,				A__      )
       or run_maze(A__     ,				A__     ,				j + 1     ,				A__      )
       or run_maze(A__     ,				i - 1     ,				A__     ,				A__      )
       or run_maze(A__     ,				A__     ,				j - 1     ,				A__      )
   ):
    return True

   __lowercase							  = 0
   return False
 return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()

