

"""simple docstring"""




from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
__UpperCAmelCase =				0

__UpperCAmelCase =				[
    [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],
]

__UpperCAmelCase =				[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

__UpperCAmelCase =				tuple[int, int]







class      __lowercase			:

  def __init__(  self		:			List[str]					,A		:			int					,A		:			int					,A		:			int					,A		:			int					,A		:			int					,A		:			Node | None					,):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					pos_x
   UpperCAmelCase__					:  int					   =					pos_y
   UpperCAmelCase__					:  List[Any]					   =					(pos_y, pos_x)
   UpperCAmelCase__					:  Any					   =					goal_x
   UpperCAmelCase__					:  Dict					   =					goal_y
   UpperCAmelCase__					:  List[Any]					   =					g_cost
   UpperCAmelCase__					:  Tuple					   =					parent
   UpperCAmelCase__					:  Optional[Any]					   =					self.calculate_heuristic()
   UpperCAmelCase__					:  Optional[int]					   =					self.g_cost + self.h_cost

  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					self.pos_x - self.goal_x
   UpperCAmelCase__					:  Dict					   =					self.pos_y - self.goal_y
   if HEURISTIC == 1:
    return abs(A    ) + abs(A    )
   else:
    return sqrt(dy**2 + dx**2    )




  def __lt__(  self		:			Tuple					,A		:			Node    ):






   '''simple docstring'''
   return self.f_cost < other.f_cost







class      __lowercase			:

  def __init__(  self		:			List[str]					,A		:			TPosition					,A		:			TPosition    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					Node(start[1]					,start[0]					,goal[1]					,goal[0]					,0					,A    )
   UpperCAmelCase__					:  List[Any]					   =					Node(goal[1]					,goal[0]					,goal[1]					,goal[0]					,99_999					,A    )

   UpperCAmelCase__					:  Dict					   =					[self.start]
   UpperCAmelCase__					:  list[Node]					   =					[]

   UpperCAmelCase__					:  int					   =					False

  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   while self.open_nodes:
    # Open Nodes are sorted using __lt__
    self.open_nodes.sort()
    UpperCAmelCase__					:  List[Any]					   =					self.open_nodes.pop(0    )

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

    self.closed_nodes.append(A    )
    UpperCAmelCase__					:  Optional[int]					   =					self.get_successors(A    )

    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(A    )
     else:
      # retrieve the best current path
      UpperCAmelCase__					:  Optional[Any]					   =					self.open_nodes.pop(self.open_nodes.index(A    )    )

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

   return [self.start.pos]

  def    __lowercase       (  self		:			List[Any]					,A		:			Node    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					[]
   for action in delta:
    UpperCAmelCase__					:  int					   =					parent.pos_x + action[1]
    UpperCAmelCase__					:  Union[str, Any]					   =					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					,parent.g_cost + 1					,A					,)    )
   return successors




  def    __lowercase       (  self		:			int					,A		:			Node | None    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					node
   UpperCAmelCase__					:  int					   =					[]
   while current_node is not None:
    path.append((current_node.pos_y, current_node.pos_x)    )
    UpperCAmelCase__					:  List[str]					   =					current_node.parent
   path.reverse()
   return path







class      __lowercase			:

  def __init__(  self		:			List[Any]					,A		:			TPosition					,A		:			TPosition    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					AStar(A					,A    )
   UpperCAmelCase__					:  int					   =					AStar(A					,A    )
   UpperCAmelCase__					:  int					   =					False

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
    self.fwd_astar.open_nodes.sort()
    self.bwd_astar.open_nodes.sort()
    UpperCAmelCase__					:  List[str]					   =					self.fwd_astar.open_nodes.pop(0    )
    UpperCAmelCase__					:  List[str]					   =					self.bwd_astar.open_nodes.pop(0    )

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

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

    UpperCAmelCase__					:  Optional[int]					   =					current_bwd_node
    UpperCAmelCase__					:  List[str]					   =					current_fwd_node

    UpperCAmelCase__					:  Optional[int]					   =					{
        self.fwd_astar: self.fwd_astar.get_successors(A    ),
        self.bwd_astar: self.bwd_astar.get_successors(A    ),
    }

    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(A    )
      else:
       # retrieve the best current path
       UpperCAmelCase__					:  Dict					   =					astar.open_nodes.pop(
           astar.open_nodes.index(A    )    )

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

   return [self.fwd_astar.start.pos]




  def    __lowercase       (  self		:			Tuple					,A		:			Node					,A		:			Node    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					self.fwd_astar.retrace_path(A    )
   UpperCAmelCase__					:  List[str]					   =					self.bwd_astar.retrace_path(A    )
   bwd_path.pop()
   bwd_path.reverse()
   UpperCAmelCase__					:  Dict					   =					fwd_path + bwd_path
   return path


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

      __UpperCAmelCase =				time.time()
      __UpperCAmelCase =				AStar(init, goal)
      __UpperCAmelCase =				a_star.search()
      __UpperCAmelCase =				time.time() - start_time
      print(F"AStar execution time = {end_time:f} seconds")

      __UpperCAmelCase =				time.time()
      __UpperCAmelCase =				BidirectionalAStar(init, goal)
      __UpperCAmelCase =				time.time() - bd_start_time
      print(F"BidirectionalAStar execution time = {bd_end_time:f} seconds")








"""simple docstring"""




from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

__UpperCAmelCase =				logging.get_logger(__name__)







@add_end_docstrings(__lowerCamelCase					)
class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			List[str]					,*A		:			Optional[int]					,**A		:			List[str]    ):






   '''simple docstring'''
   super().__init__(*A					,**A    )
   self.check_model_type(A    )

  def    __lowercase       (  self		:			Optional[int]					,A		:			Dict=None					,A		:			Optional[int]=None					,A		:			Tuple=None					,**A		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Any					   =					{}, {}
   if padding is not None:
    UpperCAmelCase__					:  Union[str, Any]					   =					padding
   if truncation is not None:
    UpperCAmelCase__					:  List[str]					   =					truncation
   if top_k is not None:
    UpperCAmelCase__					:  List[Any]					   =					top_k
   return preprocess_params, {}, postprocess_params

  def __call__(  self		:			List[Any]					,A		:			Union["Image.Image", str]					,A		:			str = None					,**A		:			Union[str, Any]    ):






   '''simple docstring'''
   if isinstance(A					,(Image.Image, str)    ) and isinstance(A					,A    ):
    UpperCAmelCase__					:  Any					   =					{"""image""": image, """question""": question}
   else:
    UpperCAmelCase__					:  Dict					   =					image
   UpperCAmelCase__					:  Dict					   =					super().__call__(A					,**A    )
   return results

  def    __lowercase       (  self		:			Optional[int]					,A		:			Optional[Any]					,A		:			Optional[Any]=False					,A		:			List[Any]=False    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					load_image(inputs["""image"""]    )
   UpperCAmelCase__					:  Optional[Any]					   =					self.tokenizer(
       inputs["""question"""]					,return_tensors=self.framework					,padding=A					,truncation=A    )
   UpperCAmelCase__					:  int					   =					self.image_processor(images=A					,return_tensors=self.framework    )
   model_inputs.update(A    )
   return model_inputs

  def    __lowercase       (  self		:			Dict					,A		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					self.model(**A    )
   return model_outputs




  def    __lowercase       (  self		:			int					,A		:			List[Any]					,A		:			List[str]=5    ):






   '''simple docstring'''
   if top_k > self.model.config.num_labels:
    UpperCAmelCase__					:  Any					   =					self.model.config.num_labels

   if self.framework == "pt":
    UpperCAmelCase__					:  Optional[int]					   =					model_outputs.logits.sigmoid()[0]
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					probs.topk(A    )
   else:
    raise ValueError(f"Unsupported framework: {self.framework}"    )

   UpperCAmelCase__					:  List[str]					   =					scores.tolist()
   UpperCAmelCase__					:  Optional[int]					   =					ids.tolist()
   return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(A					,A    )]





