






import warnings

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

class 		_snake_case  (  lowerCAmelCase__					):
   _lowercase		:    List[str]			     =							["image_processor", "tokenizer"]
   _lowercase		:    int			     =							"ViTImageProcessor"
   _lowercase		:    Union[str, Any]			     =							("CLIPTokenizer", "CLIPTokenizerFast")






   def __init__(       self		,					a=None		,					a=None		,					**a)      ->       Dict:
      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		,					a=None		,					a=None		,					a=None		,					a=None		,					**a)      ->       str:
      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:
         SCREAMING_SNAKE_CASE						= self.tokenizer(_SCREAMING_SNAKE_CASE		,					return_tensors=_SCREAMING_SNAKE_CASE		,					**_SCREAMING_SNAKE_CASE)

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






   def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       List[Any]:
      return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE		,					**_SCREAMING_SNAKE_CASE)






   def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       Dict:
      return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE		,					**_SCREAMING_SNAKE_CASE)






   @property
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,					_SCREAMING_SNAKE_CASE		,					)
      return self.image_processor_class







   @property
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,					_SCREAMING_SNAKE_CASE		,					)
      return self.image_processor












from __future__ import annotations

import time

a_		:						Tuple             =    list[tuple[int, int]]

a_		:						Optional[int]             =    [
    [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],
]

a_		:						Dict             =    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class 		_snake_case  :






			def __init__(       self		,					a		,					a		,					a		,					a		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= pos_x
						SCREAMING_SNAKE_CASE						= pos_y
						SCREAMING_SNAKE_CASE						= (pos_y, pos_x)
						SCREAMING_SNAKE_CASE						= goal_x
						SCREAMING_SNAKE_CASE						= goal_y
						SCREAMING_SNAKE_CASE						= parent



class 		_snake_case  :






			def __init__(       self		,					a		,					a)      ->       Dict:
						SCREAMING_SNAKE_CASE						= Node(start[1]		,					start[0]		,					goal[1]		,					goal[0]		,					a)
						SCREAMING_SNAKE_CASE						= Node(goal[1]		,					goal[0]		,					goal[1]		,					goal[0]		,					a)

						SCREAMING_SNAKE_CASE						= [self.start]
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Path | None:
						while self.node_queue:
									SCREAMING_SNAKE_CASE						= self.node_queue.pop(0)

									if current_node.pos == self.target.pos:
												SCREAMING_SNAKE_CASE						= True
												return self.retrace_path(a)

									SCREAMING_SNAKE_CASE						= self.get_successors(a)

									for node in successors:
												self.node_queue.append(a)

						if not self.reached:
									return [self.start.pos]
						return None






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       list[Node]:
						SCREAMING_SNAKE_CASE						= []
						for action in delta:
									SCREAMING_SNAKE_CASE						= parent.pos_x + action[1]
									SCREAMING_SNAKE_CASE						= parent.pos_y + action[0]
									if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(a) - 1):
												continue

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

									successors.append(
									    Node(a		,					a		,					self.target.pos_y		,					self.target.pos_x		,					a))
						return successors







			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Path:
						SCREAMING_SNAKE_CASE						= node
						SCREAMING_SNAKE_CASE						= []
						while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x))
									SCREAMING_SNAKE_CASE						= current_node.parent
						path.reverse()
						return path



class 		_snake_case  :






			def __init__(       self		,					a		,					a)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= BreadthFirstSearch(a		,					a)
						SCREAMING_SNAKE_CASE						= BreadthFirstSearch(a		,					a)
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Path | None:
						while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
									SCREAMING_SNAKE_CASE						= self.fwd_bfs.node_queue.pop(0)
									SCREAMING_SNAKE_CASE						= self.bwd_bfs.node_queue.pop(0)

									if current_bwd_node.pos == current_fwd_node.pos:
												SCREAMING_SNAKE_CASE						= True
												return self.retrace_bidirectional_path(
												    a		,					a)

									SCREAMING_SNAKE_CASE						= current_bwd_node
									SCREAMING_SNAKE_CASE						= current_fwd_node

									SCREAMING_SNAKE_CASE						= {
									    self.fwd_bfs: self.fwd_bfs.get_successors(a),
									    self.bwd_bfs: self.bwd_bfs.get_successors(a),
									}

									for bfs in [self.fwd_bfs, self.bwd_bfs]:
												for node in successors[bfs]:
															bfs.node_queue.append(a)

						if not self.reached:
									return [self.fwd_bfs.start.pos]
						return None







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Path:
						SCREAMING_SNAKE_CASE						= self.fwd_bfs.retrace_path(a)
						SCREAMING_SNAKE_CASE						= self.bwd_bfs.retrace_path(a)
						bwd_path.pop()
						bwd_path.reverse()
						SCREAMING_SNAKE_CASE						= fwd_path + bwd_path
						return path


if __name__ == "__main__":
				# all coordinates are given in format [y,x]
				import doctest

				doctest.testmod()
				a_		:						Union[str, Any]             =    (0, 0)
				a_		:						Any             =    (len(grid) - 1, len(grid[0]) - 1)
				for elem in grid:
								print(elem)

				a_		:						Optional[Any]             =    time.time()
				a_		:						Union[str, Any]             =    BreadthFirstSearch(init, goal)
				a_		:						Optional[Any]             =    bfs.search()
				a_		:						List[str]             =    time.time() - start_bfs_time

				print('Unidirectional BFS computation time : ', bfs_time)

				a_		:						Optional[int]             =    time.time()
				a_		:						Union[str, Any]             =    BidirectionalBreadthFirstSearch(init, goal)
				a_		:						List[Any]             =    bd_bfs.search()
				a_		:						List[str]             =    time.time() - start_bd_bfs_time

				print('Bidirectional BFS computation time : ', bd_bfs_time)




