






# 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     _a							:






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = True		,		_UpperCAmelCase = False			)      ->			str:
     UpperCamelCase_						=  scheduler
     UpperCamelCase_						=  optimizers if isinstance(_UpperCAmelCase		,		(list, tuple)			) else [optimizers]
     UpperCamelCase_						=  split_batches
     UpperCamelCase_						=  step_with_optimizer
     UpperCamelCase_						=  GradientState()




  def  _UpperCAmelCase  (  self		,		*_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			List[Any]:
     if not self.step_with_optimizer:
        # No link between scheduler and optimizer -> just step
        self.scheduler.step(*_UpperCAmelCase		,		**_UpperCAmelCase			)
        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(*_UpperCAmelCase		,		**_UpperCAmelCase			)
     else:
        # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
        # num_processes steps per training step
        UpperCamelCase_						=  AcceleratorState().num_processes
        for _ in range(_UpperCAmelCase			):
           # 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(*_UpperCAmelCase		,		**_UpperCAmelCase			)
           else:
              self.scheduler.step(*_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     return self.scheduler.get_last_lr()




  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     return self.scheduler.state_dict()




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[str]:
     self.scheduler.load_state_dict(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     return self.scheduler.get_lr()




  def  _UpperCAmelCase  (  self		,		*_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			Optional[Any]:
     return self.scheduler.print_lr(*_UpperCAmelCase		,		**_UpperCAmelCase			)












from __future__ import annotations



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    list[tuple[int, int]]:
		__lowercase						,      __lowercase											=     position
		__lowercase											=     [
		    (y + 1, x + 2),
		    (y - 1, x + 2),
		    (y + 1, x - 2),
		    (y - 1, x - 2),
		    (y + 2, x + 1),
		    (y + 2, x - 1),
		    (y - 2, x + 1),
		    (y - 2, x - 1),
		]
		__lowercase											=     []

		for position in positions:
				__lowercase						,      __lowercase											=     position
				if 0 <= y_test < n and 0 <= x_test < n:
						permissible_positions.append(snake_case							)

		return permissible_positions



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    bool:
		return not any(elem == 0 for row in board for elem in row							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    bool:

		if is_complete(snake_case							):
				return True

		for position in get_valid_pos(snake_case						,   len(snake_case							)							):
				__lowercase						,      __lowercase											=     position

				if board[y][x] == 0:
						__lowercase											=     curr + 1
						if open_knight_tour_helper(snake_case						,   snake_case						,   curr + 1							):
								return True
						__lowercase											=     0

		return False



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    list[list[int]]:
		__lowercase											=     [[0 for i in range(snake_case							)] for j in range(snake_case							)]

		for i in range(snake_case							):
				for j in range(snake_case							):
						__lowercase											=     1
						if open_knight_tour_helper(snake_case						,   (i, j)						,   1							):
								return board
						__lowercase											=     0

		__lowercase											=     F"Open Kight Tour cannot be performed on a board of size {n}"
		raise ValueError(snake_case							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




