

"""simple docstring"""






from __future__ import annotations

__lowerCamelCase										=		list[tuple[int, int]]

__lowerCamelCase										=		[
    [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],
]

__lowerCamelCase										=		([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						):
      __magic_name__     =      pos_x
      __magic_name__     =      pos_y
      __magic_name__     =      (pos_y, pos_x)
      __magic_name__     =      goal_x
      __magic_name__     =      goal_y
      __magic_name__     =      g_cost
      __magic_name__     =      parent
      __magic_name__     =      self.calculate_heuristic()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      abs(self.pos_x - self.goal_x       )
      __magic_name__     =      abs(self.pos_y - self.goal_y       )
      return dx + dy







     def __lt__(      self ,						UpperCamelCase_       ):
      return self.f_cost < other.f_cost


class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      Node(start[1] ,						start[0] ,						goal[1] ,						goal[0] ,						0 ,						UpperCamelCase_       )
      __magic_name__     =      Node(goal[1] ,						goal[0] ,						goal[1] ,						goal[0] ,						9_9999 ,						UpperCamelCase_       )

      __magic_name__     =      [self.start]
      __magic_name__     =      []

      __magic_name__     =      False







     def 	lowerCAmelCase__    (      self       ):
      while self.open_nodes:
       # Open Nodes are sorted using __lt__
       self.open_nodes.sort()
       __magic_name__     =      self.open_nodes.pop(0       )

       if current_node.pos == self.target.pos:
        __magic_name__     =      True
        return self.retrace_path(UpperCamelCase_       )

       self.closed_nodes.append(UpperCamelCase_       )
       __magic_name__     =      self.get_successors(UpperCamelCase_       )

       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(UpperCamelCase_       )
        else:
         # retrieve the best current path
         __magic_name__     =      self.open_nodes.pop(self.open_nodes.index(UpperCamelCase_       )       )

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

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







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__     =      []
      for action in delta:
       __magic_name__     =      parent.pos_x + action[1]
       __magic_name__     =      parent.pos_y + action[0]

       if not (0 <= pos_x <= len(grid[0]       ) - 1 and 0 <= pos_y <= len(UpperCamelCase_       ) - 1):
        continue

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

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







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__     =      node
      __magic_name__     =      []
      while current_node is not None:
       path.append((current_node.pos_y, current_node.pos_x)       )
       __magic_name__     =      current_node.parent
      path.reverse()
      return path


if __name__ == "__main__":
 __lowerCamelCase										=		(0, 0)
 __lowerCamelCase										=		(len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 print("------")

 __lowerCamelCase										=		GreedyBestFirst(init, goal)
 __lowerCamelCase										=		greedy_bf.search()
 if path:
  for pos_x, pos_y in path:
   __lowerCamelCase										=		2

  for elem in grid:
   print(elem)




"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase										=		logging.get_logger(__name__)
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       ['''pixel_values''']







     def __init__(      self ,						UpperCamelCase_ = True ,						UpperCamelCase_ = 32 ,						UpperCamelCase_=PILImageResampling.BILINEAR ,						UpperCamelCase_ = True ,						**UpperCamelCase_ ,						):
      __magic_name__     =      do_resize
      __magic_name__     =      do_rescale
      __magic_name__     =      size_divisor
      __magic_name__     =      resample
      super().__init__(**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_       ):
      __magic_name__   ,						__magic_name__     =      get_image_size(UpperCamelCase_       )
      # Rounds the height and width down to the closest multiple of size_divisor
      __magic_name__     =      height // size_divisor * size_divisor
      __magic_name__     =      width // size_divisor * size_divisor
      __magic_name__     =      resize(UpperCamelCase_ ,						(new_h, new_w) ,						resample=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )
      return image







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_       ):
      return rescale(image=UpperCamelCase_ ,						scale=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_=None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = ChannelDimension.FIRST ,						**UpperCamelCase_ ,						):
      __magic_name__     =      do_resize if do_resize is not None else self.do_resize
      __magic_name__     =      do_rescale if do_rescale is not None else self.do_rescale
      __magic_name__     =      size_divisor if size_divisor is not None else self.size_divisor
      __magic_name__     =      resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
       raise ValueError('''size_divisor is required for resizing'''       )

      __magic_name__     =      make_list_of_images(UpperCamelCase_       )

      if not valid_images(UpperCamelCase_       ):
       raise ValueError('''Invalid image(s)'''       )

      # All transformations expect numpy arrays.
      __magic_name__     =      [to_numpy_array(UpperCamelCase_       ) for img in images]

      if do_resize:
       __magic_name__     =      [self.resize(UpperCamelCase_ ,						size_divisor=UpperCamelCase_ ,						resample=UpperCamelCase_       ) for image in images]

      if do_rescale:
       __magic_name__     =      [self.rescale(UpperCamelCase_ ,						scale=1 / 255       ) for image in images]

      __magic_name__     =      [to_channel_dimension_format(UpperCamelCase_ ,						UpperCamelCase_       ) for image in images]

      __magic_name__     =      {'''pixel_values''': images}
      return BatchFeature(data=UpperCamelCase_ ,						tensor_type=UpperCamelCase_       )

