


import numpy as np






class _lowerCamelCase:
       def __init__(					self)       ->   Dict:




           """simple docstring"""

           _lowercase       :							List[str]      		=		(0, 0)
           _lowercase       :							Optional[int]      		=		None
           _lowercase       :							List[str]      		=		0
           _lowercase       :							str      		=		0
           _lowercase       :							Dict      		=		0
       def __eq__(					self,      lowerCamelCase)       ->   Tuple:




           """simple docstring"""

           return self.position == cell.position




       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           print(self.position)






class _lowerCamelCase:
       def __init__(					self,      lowerCamelCase=(5, 5))       ->   int:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		np.zeros(__UpperCAmelCase)
           _lowercase       :							Tuple      		=		world_size[0]
           _lowercase       :							Dict      		=		world_size[1]
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           print(self.w)




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




           """simple docstring"""

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



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						List[str]:
    _lowercase       :							List[str]      		=		[]
    _lowercase       :							Optional[Any]      		=		[]
    _open.append(_SCREAMING_SNAKE_CASE )

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

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


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









"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A											=		{
    """configuration_owlvit""": [
        """OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """OwlViTConfig""",
        """OwlViTOnnxConfig""",
        """OwlViTTextConfig""",
        """OwlViTVisionConfig""",
    ],
    """processing_owlvit""": ["""OwlViTProcessor"""],
}


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		["""OwlViTFeatureExtractor"""]
  __A											=		["""OwlViTImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """OwlViTModel""",
      """OwlViTPreTrainedModel""",
      """OwlViTTextModel""",
      """OwlViTVisionModel""",
      """OwlViTForObjectDetection""",
  ]

if TYPE_CHECKING:
  from .configuration_owlvit import (
      OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      OwlViTConfig,
      OwlViTOnnxConfig,
      OwlViTTextConfig,
      OwlViTVisionConfig,
  )
  from .processing_owlvit import OwlViTProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_owlvit import OwlViTFeatureExtractor
    from .image_processing_owlvit import OwlViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_owlvit import (
        OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OwlViTForObjectDetection,
        OwlViTModel,
        OwlViTPreTrainedModel,
        OwlViTTextModel,
        OwlViTVisionModel,
    )

else:
  import sys

  __A											=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
