





"""simple docstring"""




import warnings

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



class     _UpperCAmelCase   (   __lowerCAmelCase   ):

  """simple docstring"""


  a_      =			['''image_processor''', '''tokenizer''']
  a_      =			'''ViTImageProcessor'''
  a_      =			('''CLIPTokenizer''', '''CLIPTokenizerFast''')






  def __init__(    self			:		str ,  lowerCAmelCase_			:		Any=None ,  lowerCAmelCase_			:		Optional[int]=None ,  **lowerCAmelCase_			:		int					)							->						str:
     __lowerCAmelCase							     =     None
     if "feature_extractor" in kwargs:
        warnings.warn(
            'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
            ' instead.' ,  lowerCAmelCase_ ,  )
        __lowerCAmelCase							     =     kwargs.pop('feature_extractor'					)

     __lowerCAmelCase							     =     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__(lowerCAmelCase_ ,  lowerCAmelCase_					)






  def __call__(    self			:		Tuple ,  lowerCAmelCase_			:		Tuple=None ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		Optional[Any]=None ,  lowerCAmelCase_			:		List[str]=None ,  **lowerCAmelCase_			:		Any					)							->						Tuple:
     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:
        __lowerCAmelCase							     =     self.tokenizer(lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_ ,  **lowerCAmelCase_					)

     if visual_prompt is not None:
        __lowerCAmelCase							     =     self.image_processor(lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_ ,  **lowerCAmelCase_					)

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

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






  def     lowercase		(    self			:		List[str] ,  *lowerCAmelCase_			:		int ,  **lowerCAmelCase_			:		Dict					)							->						List[str]:
     return self.tokenizer.batch_decode(*lowerCAmelCase_ ,  **lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict ,  *lowerCAmelCase_			:		Tuple ,  **lowerCAmelCase_			:		List[str]					)							->						int:
     return self.tokenizer.decode(*lowerCAmelCase_ ,  **lowerCAmelCase_					)






  @property
  def     lowercase		(    self			:		List[Any]					)							->						List[Any]:
     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' ,  lowerCAmelCase_ ,  )
     return self.image_processor_class






  @property
  def     lowercase		(    self			:		List[Any]					)							->						List[str]:
     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' ,  lowerCAmelCase_ ,  )
     return self.image_processor








from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_snake_case  :   Dict						   =  0

_snake_case  :   Dict						   =  [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

_snake_case  :   List[str]						   =  [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

_snake_case  :   Tuple						   =  tuple[int, int]



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Node | None ,  )							->						None:
     __lowerCAmelCase							     =     pos_x
     __lowerCAmelCase							     =     pos_y
     __lowerCAmelCase							     =     (pos_y, pos_x)
     __lowerCAmelCase							     =     goal_x
     __lowerCAmelCase							     =     goal_y
     __lowerCAmelCase							     =     g_cost
     __lowerCAmelCase							     =     parent
     __lowerCAmelCase							     =     self.calculate_heuristic()
     __lowerCAmelCase							     =     self.g_cost + self.h_cost






  def     lowercase		(    self			:		Any					)							->						float:
     __lowerCAmelCase							     =     self.pos_x - self.goal_x
     __lowerCAmelCase							     =     self.pos_y - self.goal_y
     if HEURISTIC == 1:
        return abs(lowerCAmelCase_					) + abs(lowerCAmelCase_					)
     else:
        return sqrt(dy**2 + dx**2					)






  def __lt__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Node					)							->						bool:
     return self.f_cost < other.f_cost





class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		List[str] ,  lowerCAmelCase_			:		TPosition ,  lowerCAmelCase_			:		TPosition					)							->						Tuple:
     __lowerCAmelCase							     =     Node(start[1] ,  start[0] ,  goal[1] ,  goal[0] ,  0 ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     Node(goal[1] ,  goal[0] ,  goal[1] ,  goal[0] ,  9_9_9_9_9 ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     [self.start]
     __lowerCAmelCase							     =     []

     __lowerCAmelCase							     =     False






  def     lowercase		(    self			:		str					)							->						list[TPosition]:
     while self.open_nodes:
        # Open Nodes are sorted using __lt__
        self.open_nodes.sort()
        __lowerCAmelCase							     =     self.open_nodes.pop(0					)

        if current_node.pos == self.target.pos:
           return self.retrace_path(lowerCAmelCase_					)

        self.closed_nodes.append(lowerCAmelCase_					)
        __lowerCAmelCase							     =     self.get_successors(lowerCAmelCase_					)

        for child_node in successors:
           if child_node in self.closed_nodes:
              continue

           if child_node not in self.open_nodes:
              self.open_nodes.append(lowerCAmelCase_					)
           else:
              # retrieve the best current path
              __lowerCAmelCase							     =     self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase_					)					)

              if child_node.g_cost < better_node.g_cost:
                 self.open_nodes.append(lowerCAmelCase_					)
              else:
                 self.open_nodes.append(lowerCAmelCase_					)

     return [self.start.pos]






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Node					)							->						list[Node]:
     __lowerCAmelCase							     =     []
     for action in delta:
        __lowerCAmelCase							     =     parent.pos_x + action[1]
        __lowerCAmelCase							     =     parent.pos_y + action[0]
        if not (0 <= pos_x <= len(grid[0]					) - 1 and 0 <= pos_y <= len(lowerCAmelCase_					) - 1):
           continue

        if grid[pos_y][pos_x] != 0:
           continue

        successors.append(
            Node(
                lowerCAmelCase_ ,  lowerCAmelCase_ ,  self.target.pos_y ,  self.target.pos_x ,  parent.g_cost + 1 ,  lowerCAmelCase_ ,  )					)
     return successors






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Node | None					)							->						list[TPosition]:
     __lowerCAmelCase							     =     node
     __lowerCAmelCase							     =     []
     while current_node is not None:
        path.append((current_node.pos_y, current_node.pos_x)					)
        __lowerCAmelCase							     =     current_node.parent
     path.reverse()
     return path





class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		int ,  lowerCAmelCase_			:		TPosition ,  lowerCAmelCase_			:		TPosition					)							->						None:
     __lowerCAmelCase							     =     AStar(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     AStar(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     False






  def     lowercase		(    self			:		Dict					)							->						list[TPosition]:
     while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
        self.fwd_astar.open_nodes.sort()
        self.bwd_astar.open_nodes.sort()
        __lowerCAmelCase							     =     self.fwd_astar.open_nodes.pop(0					)
        __lowerCAmelCase							     =     self.bwd_astar.open_nodes.pop(0					)

        if current_bwd_node.pos == current_fwd_node.pos:
           return self.retrace_bidirectional_path(
               lowerCAmelCase_ ,  lowerCAmelCase_					)

        self.fwd_astar.closed_nodes.append(lowerCAmelCase_					)
        self.bwd_astar.closed_nodes.append(lowerCAmelCase_					)

        __lowerCAmelCase							     =     current_bwd_node
        __lowerCAmelCase							     =     current_fwd_node

        __lowerCAmelCase							     =     {
            self.fwd_astar: self.fwd_astar.get_successors(lowerCAmelCase_					),
            self.bwd_astar: self.bwd_astar.get_successors(lowerCAmelCase_					),
        }

        for astar in [self.fwd_astar, self.bwd_astar]:
           for child_node in successors[astar]:
              if child_node in astar.closed_nodes:
                 continue

              if child_node not in astar.open_nodes:
                 astar.open_nodes.append(lowerCAmelCase_					)
              else:
                 # retrieve the best current path
                 __lowerCAmelCase							     =     astar.open_nodes.pop(
                     astar.open_nodes.index(lowerCAmelCase_					)					)

                 if child_node.g_cost < better_node.g_cost:
                    astar.open_nodes.append(lowerCAmelCase_					)
                 else:
                    astar.open_nodes.append(lowerCAmelCase_					)

     return [self.fwd_astar.start.pos]






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Node ,  lowerCAmelCase_			:		Node					)							->						list[TPosition]:
     __lowerCAmelCase							     =     self.fwd_astar.retrace_path(lowerCAmelCase_					)
     __lowerCAmelCase							     =     self.bwd_astar.retrace_path(lowerCAmelCase_					)
     bwd_path.pop()
     bwd_path.reverse()
     __lowerCAmelCase							     =     fwd_path + bwd_path
     return path


if __name__ == "__main__":
     # all coordinates are given in format [y,x]
     _snake_case  :   List[Any]						   =  (0, 0)
     _snake_case  :   Union[str, Any]						   =  (len(grid) - 1, len(grid[0]) - 1)
     for elem in grid:
          print(elem)

     _snake_case  :   int						   =  time.time()
     _snake_case  :   Optional[int]						   =  AStar(init, goal)
     _snake_case  :   int						   =  a_star.search()
     _snake_case  :   Union[str, Any]						   =  time.time() - start_time
     print(F"""AStar execution time = {end_time:f} seconds""")

     _snake_case  :   Any						   =  time.time()
     _snake_case  :   Dict						   =  BidirectionalAStar(init, goal)
     _snake_case  :   Optional[int]						   =  time.time() - bd_start_time
     print(F"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")

