






import numpy as np


class     _a							:






  """simple docstring"""





  def __init__(  self			)      ->			Union[str, Any]:
     UpperCamelCase_						=  (0, 0)
     UpperCamelCase_						=  None
     UpperCamelCase_						=  0
     UpperCamelCase_						=  0
     UpperCamelCase_						=  0




  def __eq__(  self		,		_UpperCAmelCase			)      ->			Optional[int]:
     return self.position == cell.position




  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     print(self.position			)




class     _a							:






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase=(5, 5)			)      ->			str:
     UpperCamelCase_						=  np.zeros(_UpperCAmelCase			)
     UpperCamelCase_						=  world_size[0]
     UpperCamelCase_						=  world_size[1]




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     print(self.w			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[str]:
     UpperCamelCase_						=  [
         (-1, -1),
         (-1, 0),
         (-1, 1),
         (0, -1),
         (0, 1),
         (1, -1),
         (1, 0),
         (1, 1),
     ]
     UpperCamelCase_						=  cell.position[0]
     UpperCamelCase_						=  cell.position[1]
     UpperCamelCase_						=  []
     for n in neughbour_cord:
        UpperCamelCase_						=  current_x + n[0]
        UpperCamelCase_						=  current_y + n[1]
        if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
           UpperCamelCase_						=  Cell()
           UpperCamelCase_						=  (x, y)
           UpperCamelCase_						=  cell
           neighbours.append(_UpperCAmelCase			)
     return neighbours






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  []
   UpperCamelCase_						=  []
   _open.append(__lowercase)

   while _open:
      UpperCamelCase_						=  np.argmin([n.f for n in _open])
      UpperCamelCase_						=  _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
         UpperCamelCase_						=  current.g + 1
         UpperCamelCase_		,   UpperCamelCase_						=  n.position
         UpperCamelCase_		,   UpperCamelCase_						=  goal.position
         UpperCamelCase_						=  (ya - ya) ** 2 + (xa - xa) ** 2
         UpperCamelCase_						=  n.h + n.g

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


if __name__ == "__main__":
 snake_case__   :						List[str]													=   Gridworld()
 # Start position and goal
 snake_case__   :						int													=   Cell()
 snake_case__   :						Dict													=   (0, 0)
 snake_case__   :						Optional[int]													=   Cell()
 snake_case__   :						str													=   (4, 4)
 print(f'path from {start.position} to {goal.position}')
 snake_case__   :						Tuple													=   astar(world, start, goal)
 #   Just for visual reasons.
 for i in s:
  snake_case__   :						List[str]													=   1
 print(world.w)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__   :						Optional[int]													=   {
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Dict													=   [
     """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """PegasusXForConditionalGeneration""",
     """PegasusXModel""",
     """PegasusXPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_pegasus_x import (
      PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
      PegasusXForConditionalGeneration,
      PegasusXModel,
      PegasusXPreTrainedModel,
  )


else:
 import sys

 snake_case__   :						Optional[Any]													=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





