


"""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 _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__ = True		,			UpperCamelCase__ = False		)			->	str:

       '''simple docstring'''




       snake_case					:   Tuple         =					scheduler
       snake_case					:   Any         =					optimizers if isinstance(UpperCamelCase__		,			(list, tuple)		) else [optimizers]
       snake_case					:   List[str]         =					split_batches
       snake_case					:   Optional[int]         =					step_with_optimizer
       snake_case					:   List[str]         =					GradientState()







   def 				lowerCamelCase	(			self		,			*UpperCamelCase__		,			**UpperCamelCase__		)			->	List[Any]:

       '''simple docstring'''




       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
           snake_case					:   Tuple         =					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 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       return self.scheduler.get_last_lr()







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       return self.scheduler.state_dict()







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Tuple:

       '''simple docstring'''




       self.scheduler.load_state_dict(UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       return self.scheduler.get_lr()







   def 				lowerCamelCase	(			self		,			*UpperCamelCase__		,			**UpperCamelCase__		)			->	Optional[Any]:

       '''simple docstring'''




       return self.scheduler.print_lr(*UpperCamelCase__		,			**UpperCamelCase__		)









"""simple docstring"""



import numpy as np







class _lowerCAmelCase       :







   def __init__(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					(0, 0)
       snake_case					:   str         =					None
       snake_case					:   int         =					0
       snake_case					:   Optional[Any]         =					0
       snake_case					:   Tuple         =					0







   def __eq__(			self		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       return self.position == cell.position







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       print(self.position		)







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__=(5, 5)		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					np.zeros(UpperCamelCase__		)
       snake_case					:   str         =					world_size[0]
       snake_case					:   str         =					world_size[1]







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       print(self.w		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					[
           (-1, -1),
           (-1, 0),
           (-1, 1),
           (0, -1),
           (0, 1),
           (1, -1),
           (1, 0),
           (1, 1),
       ]
       snake_case					:   Tuple         =					cell.position[0]
       snake_case					:   str         =					cell.position[1]
       snake_case					:   List[Any]         =					[]
       for n in neughbour_cord:
           snake_case					:   Any         =					current_x + n[0]
           snake_case					:   List[str]         =					current_y + n[1]
           if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
               snake_case					:   Tuple         =					Cell()
               snake_case					:   int         =					(x, y)
               snake_case					:   List[Any]         =					cell
               neighbours.append(UpperCamelCase__		)
       return neighbours







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]		,							lowercase						:						Union[str, Any]		,							lowercase						:						Union[str, Any]				)			->		Any:


    """simple docstring"""





    snake_case					:   Tuple         =					[]
    snake_case					:   List[Any]         =					[]
    _open.append(lowercase				)

    while _open:
        snake_case					:   Union[str, Any]         =					np.argmin([n.f for n in _open]				)
        snake_case					:   Tuple         =					_open[min_f]
        _closed.append(_open.pop(lowercase				)				)
        if current == goal:
            break
        for n in world.get_neigbours(lowercase				):
            for c in _closed:
                if c == n:
                    continue
            snake_case					:   int         =					current.g + 1
            snake_case      ,snake_case					:   Any         =					n.position
            snake_case      ,snake_case					:   Any         =					goal.position
            snake_case					:   int         =					(ya - ya) ** 2 + (xa - xa) ** 2
            snake_case					:   List[Any]         =					n.h + n.g

            for c in _open:
                if c == n and c.f < n.f:
                    continue
            _open.append(lowercase				)
    snake_case					:   str         =					[]
    while current.parent is not None:
        path.append(current.position				)
        snake_case					:   List[str]         =					current.parent
    path.append(current.position				)
    return path[::-1]


if __name__ == "__main__":
  __snake_case         =					Gridworld()
  # Start position and goal
  __snake_case         =					Cell()
  __snake_case         =					(0, 0)
  __snake_case         =					Cell()
  __snake_case         =					(4, 4)
  print(F'''path from {start.position} to {goal.position}''')
  __snake_case         =					astar(world, start, goal)
  #   Just for visual reasons.
  for i in s:
    __snake_case         =					1
  print(world.w)





