




"""simple docstring"""



import numpy as np




class      UpperCAmelCase_  :




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




        '''simple docstring'''

        A__	=    (0, 0)
        A__	=    None
        A__	=    0
        A__	=    0
        A__	=    0




    def __eq__(   self						:	Dict     ,			snake_case_						:	int				)				->							int:




        '''simple docstring'''

        return self.position == cell.position




    def 							__magic_name__							(   self						:	List[str]				)				->							Union[str, Any]:




        '''simple docstring'''

        print(self.position				)






class      UpperCAmelCase_  :




    def __init__(   self						:	Dict     ,			snake_case_						:	Optional[int]=(5, 5)				)				->							Tuple:




        '''simple docstring'''

        A__	=    np.zeros(snake_case_				)
        A__	=    world_size[0]
        A__	=    world_size[1]




    def 							__magic_name__							(   self						:	Optional[int]				)				->							int:




        '''simple docstring'''

        print(self.w				)




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	List[str]				)				->							Union[str, Any]:




        '''simple docstring'''

        A__	=    [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        A__	=    cell.position[0]
        A__	=    cell.position[1]
        A__	=    []
        for n in neughbour_cord:
            A__	=    current_x + n[0]
            A__	=    current_y + n[1]
            if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                A__	=    Cell()
                A__	=    (x, y)
                A__	=    cell
                neighbours.append(snake_case_				)
        return neighbours







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		Tuple:
    A__	=    []
    A__	=    []
    _open.append(lowercase_						)

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

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


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











"""simple docstring"""



import warnings

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




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				['''image_processor''', '''tokenizer''']
    lowercase__						 =				'''ViTImageProcessor'''
    lowercase__						 =				('''CLIPTokenizer''', '''CLIPTokenizerFast''')




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	Dict=None     ,			**snake_case_						:	Optional[Any]				)				->							List[str]:




     '''simple docstring'''

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

     A__	=    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__(snake_case_     ,			snake_case_				)




    def __call__(   self						:	int     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	int=None     ,			snake_case_						:	Dict=None     ,			snake_case_						:	int=None     ,			**snake_case_						:	List[str]				)				->							Union[str, Any]:




     '''simple docstring'''

     if text is None and visual_prompt is None and images is None:
      raise ValueError("You have to specify either text, visual prompt or images."				)

     if text is not None and visual_prompt is not None:
      raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."				)

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

     if visual_prompt is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

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

     if visual_prompt is not None and images is not None:
      A__	=    {
          "pixel_values": image_features.pixel_values,
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     elif text is not None and images is not None:
      A__	=    image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     elif visual_prompt is not None:
      A__	=    {
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case_				)     ,			tensor_type=snake_case_				)




    def 							__magic_name__							(   self						:	Tuple     ,			*snake_case_						:	List[str]     ,			**snake_case_						:	Optional[int]				)				->							str:




     '''simple docstring'''

     return self.tokenizer.batch_decode(*snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]     ,			*snake_case_						:	Any     ,			**snake_case_						:	List[Any]				)				->							List[str]:




     '''simple docstring'''

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




    @property
    def 							__magic_name__							(   self						:	int				)				->							Optional[int]:




     '''simple docstring'''

     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,			snake_case_     ,			)
     return self.image_processor_class




    @property
    def 							__magic_name__							(   self						:	int				)				->							int:




     '''simple docstring'''

     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,			snake_case_     ,			)
     return self.image_processor





