


import numpy as np



class 							_UpperCamelCase :

   '''simple docstring'''







   def __init__(  self  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						(0, 0)
         __lowerCamelCase     :      List[str]       						=						None
         __lowerCamelCase     :      List[str]       						=						0
         __lowerCamelCase     :      Optional[Any]       						=						0
         __lowerCamelCase     :      List[str]       						=						0

   def __eq__(  self  : Tuple       ,  _lowerCamelCase  : Dict							):






         '''simple docstring'''



         return self.position == cell.position


   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         print(self.position							)



class 							_UpperCamelCase :

   '''simple docstring'''







   def __init__(  self  : str       ,  _lowerCamelCase  : List[Any]=(5, 5)							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						np.zeros(_lowerCamelCase							)
         __lowerCamelCase     :      int       						=						world_size[0]
         __lowerCamelCase     :      List[Any]       						=						world_size[1]

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         print(self.w							)


   def   _snake_case    (  self  : str       ,  _lowerCamelCase  : Tuple							):






         '''simple docstring'''



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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			str      ):
      """simple docstring"""




      __lowerCamelCase     :      str       						=						[]
      __lowerCamelCase     :      Union[str, Any]       						=						[]
      _open.append(UpperCAmelCase      )

      while _open:
            __lowerCamelCase     :      Tuple       						=						np.argmin([n.f for n in _open]      )
            __lowerCamelCase     :      List[str]       						=						_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
                  __lowerCamelCase     :      Any       						=						current.g + 1
                  __lowerCamelCase     :      List[str]       						=						n.position
                  __lowerCamelCase     :      Tuple       						=						goal.position
                  __lowerCamelCase     :      Optional[int]       						=						(ya - ya) ** 2 + (xa - xa) ** 2
                  __lowerCamelCase     :      Optional[int]       						=						n.h + n.g

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


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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    :						Optional[Any]     =							{
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[Any]     =							[
        'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'Swinv2ForImageClassification',
        'Swinv2ForMaskedImageModeling',
        'Swinv2Model',
        'Swinv2PreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_swinva import (
            SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            SwinvaForImageClassification,
            SwinvaForMaskedImageModeling,
            SwinvaModel,
            SwinvaPreTrainedModel,
        )


else:
    import sys

    __UpperCamelCase    :						List[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



