import numpy as np





class 	A				:


   def __init__(self	:		Any						)		->							List[str]:
         """simple docstring"""



         UpperCAmelCase__								=    (0, 0)
         UpperCAmelCase__								=    None
         UpperCAmelCase__								=    0
         UpperCAmelCase__								=    0
         UpperCAmelCase__								=    0


   def __eq__(self	:		Tuple			,    __UpperCAmelCase	:		Optional[int]						)		->							str:
         """simple docstring"""



         return self.position == cell.position





   def      lowercase_ (self	:		str						)		->							str:
         """simple docstring"""



         print(self.position						)





class 	A				:


   def __init__(self	:		str			,    __UpperCAmelCase	:		Tuple=(5, 5)						)		->							int:
         """simple docstring"""



         UpperCAmelCase__								=    np.zeros(__SCREAMING_SNAKE_CASE						)
         UpperCAmelCase__								=    world_size[0]
         UpperCAmelCase__								=    world_size[1]


   def      lowercase_ (self	:		Union[str, Any]						)		->							List[Any]:
         """simple docstring"""



         print(self.w						)





   def      lowercase_ (self	:		List[Any]			,    __UpperCAmelCase	:		List[Any]						)		->							Dict:
         """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   lowerCAmelCase_					(	__A,    __A,    __A				)	->							Any:







      '''simple docstring'''


      UpperCAmelCase__								=    []
      UpperCAmelCase__								=    []
      _open.append(a__				)

      while _open:
            UpperCAmelCase__								=    np.argmin([n.f for n in _open]				)
            UpperCAmelCase__								=    _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
                  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(a__				)
      UpperCAmelCase__								=    []
      while current.parent is not None:
            path.append(current.position				)
            UpperCAmelCase__								=    current.parent
      path.append(current.position				)
      return path[::-1]


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






'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "CLIPImageProcessor"
 lowerCAmelCase__       = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
 def __init__(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				**__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = None
      if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
           __SCREAMING_SNAKE_CASE      = kwargs.pop("""feature_extractor"""     )

      __SCREAMING_SNAKE_CASE      = image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
           raise ValueError("""You need to specify an `image_processor`."""     )
      if tokenizer is None:
           raise ValueError("""You need to specify a `tokenizer`."""     )

      super().__init__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					int:


      """simple docstring"""




      if text is None and images is None:
           raise ValueError("""You have to specify either text or images. Both cannot be none."""     )

      if text is not None:
           __SCREAMING_SNAKE_CASE      = self.tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      if images is not None:
           __SCREAMING_SNAKE_CASE      = self.image_processor(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      if text is not None and images is not None:
           __SCREAMING_SNAKE_CASE      = image_features.pixel_values
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE     )   ,				tensor_type=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[str]   ,				*__SCREAMING_SNAKE_CASE   :			Tuple   ,				**__SCREAMING_SNAKE_CASE   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]   ,				*__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					Optional[Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )


 @property
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.tokenizer.model_input_names
      __SCREAMING_SNAKE_CASE      = self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )






