






import numpy as np





class 					A__     :

     '''simple docstring'''






     def __init__( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  (0, 0)
           UpperCamelCase					       =  None
           UpperCamelCase					       =  0
           UpperCamelCase					       =  0
           UpperCamelCase					       =  0







     def __eq__( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    List[str] ):






           """simple docstring"""




           return self.position == cell.position







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           print(self.position )





class 					A__     :

     '''simple docstring'''






     def __init__( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    int=(5, 5) ):






           """simple docstring"""




           UpperCamelCase					       =  np.zeros(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  world_size[0]
           UpperCamelCase					       =  world_size[1]







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           print(self.w )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           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(_SCREAMING_SNAKE_CASE )
           return neighbours

def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Union[str, Any]:
      """simple docstring"""

      UpperCamelCase					       =  []
      UpperCamelCase					       =  []
      _open.append(_UpperCamelCase)

      while _open:
            UpperCamelCase					       =  np.argmin([n.f for n in _open])
            UpperCamelCase					       =  _open[min_f]
            _closed.append(_open.pop(_UpperCamelCase))
            if current == goal:
                  break
            for n in world.get_neigbours(_UpperCamelCase):
                  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(_UpperCamelCase)
      UpperCamelCase					       =  []
      while current.parent is not None:
            path.append(current.position)
            UpperCamelCase					       =  current.parent
      path.append(current.position)
      return path[::-1]


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











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__   :  Tuple        =							{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __magic_name__   :  Optional[Any]        =							['''PLBartTokenizer''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __magic_name__   :  Tuple        =							[
           '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''PLBartForCausalLM''',
           '''PLBartForConditionalGeneration''',
           '''PLBartForSequenceClassification''',
           '''PLBartModel''',
           '''PLBartPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_plbart import PLBartTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_plbart import (
                  PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PLBartForCausalLM,
                  PLBartForConditionalGeneration,
                  PLBartForSequenceClassification,
                  PLBartModel,
                  PLBartPreTrainedModel,
              )


else:
       import sys

       __magic_name__   :  Optional[int]        =							_LazyModule(__name__, globals()['''__file__'''], _import_structure)



