from __future__ import annotations

__lowerCamelCase			: Union[str, Any]							   =		[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       list[list[int]] ,     __UpperCamelCase    :       list[int] ,     __UpperCamelCase    :       list[int] ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       list[list[int]] ,     )							->					tuple[list[list[int]], list[list[int]]]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      [
      [0 for col in range(len(grid[0]	)	)] for row in range(len(__UpperCamelCase	)	)
  ]  # the reference grid
  SCREAMING_SNAKE_CASE__										=      1
  SCREAMING_SNAKE_CASE__										=      [
      [0 for col in range(len(grid[0]	)	)] for row in range(len(__UpperCamelCase	)	)
  ]  # the action grid

  SCREAMING_SNAKE_CASE__										=      init[0]
  SCREAMING_SNAKE_CASE__										=      init[1]
  SCREAMING_SNAKE_CASE__										=      0
  SCREAMING_SNAKE_CASE__										=      g + heuristic[x][y]  # cost from starting cell to destination cell
  SCREAMING_SNAKE_CASE__										=      [[f, g, x, y]]

  SCREAMING_SNAKE_CASE__										=      False  # flag that is set when search is complete
  SCREAMING_SNAKE_CASE__										=      False  # flag set if we can't find expand

  while not found and not resign:
    if len(__UpperCamelCase	) == 0:
      raise ValueError("""Algorithm is unable to find solution"""	)
    else:  # to choose the least costliest action so as to move closer to the goal
      cell.sort()
      cell.reverse()
      SCREAMING_SNAKE_CASE__										=      cell.pop()
      SCREAMING_SNAKE_CASE__										=      next_cell[2]
      SCREAMING_SNAKE_CASE__										=      next_cell[3]
      SCREAMING_SNAKE_CASE__										=      next_cell[1]

      if x == goal[0] and y == goal[1]:
        SCREAMING_SNAKE_CASE__										=      True
      else:
        for i in range(len(__UpperCamelCase	)	):  # to try out different valid actions
          SCREAMING_SNAKE_CASE__										=      x + DIRECTIONS[i][0]
          SCREAMING_SNAKE_CASE__										=      y + DIRECTIONS[i][1]
          if xa >= 0 and xa < len(__UpperCamelCase	) and ya >= 0 and ya < len(grid[0]	):
            if closed[xa][ya] == 0 and grid[xa][ya] == 0:
              SCREAMING_SNAKE_CASE__										=      g + cost
              SCREAMING_SNAKE_CASE__										=      ga + heuristic[xa][ya]
              cell.append([fa, ga, xa, ya]	)
              SCREAMING_SNAKE_CASE__										=      1
              SCREAMING_SNAKE_CASE__										=      i
  SCREAMING_SNAKE_CASE__										=      []
  SCREAMING_SNAKE_CASE__										=      goal[0]
  SCREAMING_SNAKE_CASE__										=      goal[1]
  invpath.append([x, y]	)  # we get the reverse path from here
  while x != init[0] or y != init[1]:
    SCREAMING_SNAKE_CASE__										=      x - DIRECTIONS[action[x][y]][0]
    SCREAMING_SNAKE_CASE__										=      y - DIRECTIONS[action[x][y]][1]
    SCREAMING_SNAKE_CASE__										=      xa
    SCREAMING_SNAKE_CASE__										=      ya
    invpath.append([x, y]	)

  SCREAMING_SNAKE_CASE__										=      []
  for i in range(len(__UpperCamelCase	)	):
    path.append(invpath[len(__UpperCamelCase	) - 1 - i]	)
  return path, action


if __name__ == "__main__":
   __lowerCamelCase			: Tuple							   =		[
       [0, 1, 0, 0, 0, 0],
       [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
       [0, 1, 0, 0, 0, 0],
       [0, 1, 0, 0, 1, 0],
       [0, 0, 0, 0, 1, 0],
   ]

   __lowerCamelCase			: Union[str, Any]							   =		[0, 0]
   # all coordinates are given in format [y,x]
   __lowerCamelCase			: Tuple							   =		[len(grid) - 1, len(grid[0]) - 1]
   __lowerCamelCase			: List[Any]							   =		1

   # the cost map which pushes the path closer to the goal
   __lowerCamelCase			: Dict							   =		[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
   for i in range(len(grid)):
      for j in range(len(grid[0])):
         __lowerCamelCase			: Tuple							   =		abs(i - goal[0]) + abs(j - goal[1])
         if grid[i][j] == 1:
            # added extra penalty in the heuristic map
            __lowerCamelCase			: int							   =		99

   __lowerCamelCase   ,						__lowerCamelCase			: Any							   =		search(grid, init, goal, cost, heuristic)

   print('''ACTION MAP''')
   for i in range(len(action)):
      print(action[i])

   for i in range(len(path)):
      print(path[i])

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			: Union[str, Any]							   =		logging.get_logger(__name__)







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(    self			:				Any       ,	_lowercase			:				bool = True       ,	_lowercase			:				int = 32       ,	_lowercase			:				Any=PILImageResampling.BILINEAR       ,	_lowercase			:				bool = True       ,	**_lowercase			:				Union[str, Any]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor
     SCREAMING_SNAKE_CASE__										=      resample
     super().__init__(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				int       ,	_lowercase			:				int       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      get_image_size(_lowercase      )
     # Rounds the height and width down to the closest multiple of size_divisor
     SCREAMING_SNAKE_CASE__										=      height // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      width // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      resize(_lowercase       ,	(new_h, new_w)       ,	resample=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )
     return image




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				float       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				str      ):



     """simple docstring"""






     return rescale(image=_lowercase       ,	scale=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				int=None       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[Union[TensorType, str]] = None       ,	_lowercase			:				ChannelDimension = ChannelDimension.FIRST       ,	**_lowercase			:				Optional[int]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor if size_divisor is not None else self.size_divisor
     SCREAMING_SNAKE_CASE__										=      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"""      )

     SCREAMING_SNAKE_CASE__										=      make_list_of_images(_lowercase      )

     if not valid_images(_lowercase      ):
       raise ValueError("""Invalid image(s)"""      )

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

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

     if do_rescale:
       SCREAMING_SNAKE_CASE__										=      [self.rescale(_lowercase       ,	scale=1 / 2_55      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      [to_channel_dimension_format(_lowercase       ,	_lowercase      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      {"""pixel_values""": images}
     return BatchFeature(data=_lowercase       ,	tensor_type=_lowercase      )
