
'''simple docstring'''


from __future__ import annotations

snake_case_ :  Union[str, Any]     =					list[tuple[int, int]]

snake_case_ :  str     =					[
    [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_ :  Dict     =					([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right


class 						lowercase__	:






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  pos_x
      UpperCamelCase       =  pos_y
      UpperCamelCase       =  (pos_y, pos_x)
      UpperCamelCase       =  goal_x
      UpperCamelCase       =  goal_y
      UpperCamelCase       =  g_cost
      UpperCamelCase       =  parent
      UpperCamelCase       =  self.calculate_heuristic()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  abs(self.pos_x - self.goal_x   )
      UpperCamelCase       =  abs(self.pos_y - self.goal_y   )
      return dx + dy







   def __lt__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.f_cost < other.f_cost




class 						lowercase__	:






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  Node(start[1] ,   start[0] ,   goal[1] ,   goal[0] ,   0 ,   lowerCamelCase__   )
      UpperCamelCase       =  Node(goal[1] ,   goal[0] ,   goal[1] ,   goal[0] ,   9_9_9_9_9 ,   lowerCamelCase__   )

      UpperCamelCase       =  [self.start]
      UpperCamelCase       =  []

      UpperCamelCase       =  False







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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

         if current_node.pos == self.target.pos:
            UpperCamelCase       =  True
            return self.retrace_path(lowerCamelCase__   )

         self.closed_nodes.append(lowerCamelCase__   )
         UpperCamelCase       =  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
               UpperCamelCase       =  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__   )

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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  []
      for action in delta:
         UpperCamelCase       =  parent.pos_x + action[1]
         UpperCamelCase       =  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       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  node
      UpperCamelCase       =  []
      while current_node is not None:
         path.append((current_node.pos_y, current_node.pos_x)   )
         UpperCamelCase       =  current_node.parent
      path.reverse()
      return path


if __name__ == "__main__":
   snake_case_ :  Union[str, Any]     =					(0, 0)
   snake_case_ :  Dict     =					(len(grid) - 1, len(grid[0]) - 1)
   for elem in grid:
      print(elem)

   print('------')

   snake_case_ :  Dict     =					GreedyBestFirst(init, goal)
   snake_case_ :  Optional[int]     =					greedy_bf.search()
   if path:
      for pos_x, pos_y in path:
         snake_case_ :  Optional[int]     =					2

      for elem in grid:
         print(elem)







'''simple docstring'''


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import LevitImageProcessor


class 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=7 ,   lowerCamelCase__=3 ,   lowerCamelCase__=1_8 ,   lowerCamelCase__=3_0 ,   lowerCamelCase__=4_0_0 ,   lowerCamelCase__=True ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   lowerCamelCase__=[0.5, 0.5, 0.5] ,   lowerCamelCase__=[0.5, 0.5, 0.5] ,   ):

      '''simple docstring'''







      UpperCamelCase       =  size if size is not None else {'''shortest_edge''': 1_8}
      UpperCamelCase       =  crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
      UpperCamelCase       =  parent
      UpperCamelCase       =  batch_size
      UpperCamelCase       =  num_channels
      UpperCamelCase       =  image_size
      UpperCamelCase       =  min_resolution
      UpperCamelCase       =  max_resolution
      UpperCamelCase       =  do_resize
      UpperCamelCase       =  size
      UpperCamelCase       =  do_center_crop
      UpperCamelCase       =  crop_size
      UpperCamelCase       =  do_normalize
      UpperCamelCase       =  image_mean
      UpperCamelCase       =  image_std







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "do_center_crop": self.do_center_crop,
          "size": self.size,
          "crop_size": self.crop_size,
      }




@require_torch
@require_vision
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							LevitImageProcessor if is_vision_available() else None







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  LevitImageProcessingTester(self   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.image_processor_tester.prepare_image_processor_dict()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''image_mean'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''image_std'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_normalize'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_resize'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_center_crop'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''size'''   )   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict   )
      self.assertEqual(image_processor.size ,   {'''shortest_edge''': 1_8}   )
      self.assertEqual(image_processor.crop_size ,   {'''height''': 1_8, '''width''': 1_8}   )

      UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict ,   size=4_2 ,   crop_size=8_4   )
      self.assertEqual(image_processor.size ,   {'''shortest_edge''': 4_2}   )
      self.assertEqual(image_processor.crop_size ,   {'''height''': 8_4, '''width''': 8_4}   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      pass







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   Image.Image   )

      # Test not batched input
      UpperCamelCase       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase__ ,   numpify=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   np.ndarray   )

      # Test not batched input
      UpperCamelCase       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random PyTorch tensors
      UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase__ ,   torchify=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   torch.Tensor   )

      # Test not batched input
      UpperCamelCase       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )





