


"""simple docstring"""







import numpy as np






class 						_A  :







    """simple docstring"""




    def __init__( self    :   int):
      a						:		Optional[Any]											=      (0, 0)
      a						:		Any											=      None
      a						:		Union[str, Any]											=      0
      a						:		List[str]											=      0
      a						:		Optional[Any]											=      0






    def __eq__( self    :   int    ,			__UpperCAmelCase    :   Optional[Any]):
      return self.position == cell.position






    def      __snake_case							( self    :   int):
      print(self.position)






class 						_A  :







    """simple docstring"""




    def __init__( self    :   int    ,			__UpperCAmelCase    :   Union[str, Any]=(5, 5)):
      a						:		str											=      np.zeros(__UpperCAmelCase)
      a						:		Tuple											=      world_size[0]
      a						:		str											=      world_size[1]






    def      __snake_case							( self    :   Optional[Any]):
      print(self.w)






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






def 					lowercase      (    A_  ,       A_  ,       A_	)->				int:
  '''simple docstring'''







  a						:		Tuple											=      []
  a						:		str											=      []
  _open.append(A_	)

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

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


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










"""simple docstring"""







__lowercase    =				frozenset(
    [
        """prompt""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """negative_prompt"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(["""image"""])

__lowercase    =				frozenset(
    [
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""image"""])

__lowercase    =				frozenset(
    [
        """prompt""",
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """image""", """negative_prompt"""])

__lowercase    =				frozenset(
    [
        # Text guided image variation with an image mask
        """prompt""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])

__lowercase    =				frozenset(
    [
        # image variation with an image mask
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""image""", """mask_image"""])

__lowercase    =				frozenset(
    [
        """example_image""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""example_image""", """image""", """mask_image"""])

__lowercase    =				frozenset(["""class_labels"""])

__lowercase    =				frozenset(["""class_labels"""])

__lowercase    =				frozenset(["""batch_size"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(["""batch_size"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(
    [
        """prompt""",
        """audio_length_in_s""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """negative_prompt"""])
__lowercase    =				frozenset(["""input_tokens"""])

__lowercase    =				frozenset(["""input_tokens"""])






