




import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor
class 						_A (  unittest.TestCase				):






  def __init__(  self		:     Optional[Any]				,						_A		:     Union[str, Any]				,						_A		:     Optional[int]=7				,						_A		:     Union[str, Any]=3				,						_A		:     List[Any]=18				,						_A		:     int=30				,						_A		:     Union[str, Any]=400				,						_A		:     List[str]=True				,						_A		:     Union[str, Any]=None				,						_A		:     Dict=True				,						_A		:     str=None				,						_A		:     Optional[Any]=True				,						_A		:     Optional[Any]=[0.5, 0.5, 0.5]				,						_A		:     Any=[0.5, 0.5, 0.5]				,						_A		:     Dict=False				,						)		->   Dict:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			size if size is not None else {'''height''': 20, '''width''': 20}
       lowercase :       List[str]       =			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
       lowercase :       Optional[Any]       =			parent
       lowercase :       List[Any]       =			batch_size
       lowercase :       List[str]       =			num_channels
       lowercase :       int       =			image_size
       lowercase :       Union[str, Any]       =			min_resolution
       lowercase :       Any       =			max_resolution
       lowercase :       Optional[int]       =			do_resize
       lowercase :       List[Any]       =			size
       lowercase :       Optional[Any]       =			do_center_crop
       lowercase :       Optional[int]       =			crop_size
       lowercase :       Optional[int]       =			do_normalize
       lowercase :       List[str]       =			image_mean
       lowercase :       Optional[Any]       =			image_std
       lowercase :       Optional[int]       =			do_reduce_labels






  def  __a      (  self		:     Optional[int]				)		->   Any:






       """simple docstring"""







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






def  snake_case(    )			->							List[Any]:





     '''simple docstring'''


     lowercase :       Any       =			load_dataset('''hf-internal-testing/fixtures_ade20k'''			,		split='''test'''   )

     lowercase :       List[str]       =			Image.open(dataset[0]['''file''']   )
     lowercase :       str       =			Image.open(dataset[1]['''file''']   )

     return image, map






def  snake_case(    )			->							Dict:





     '''simple docstring'''


     lowercase :       List[str]       =			load_dataset('''hf-internal-testing/fixtures_ade20k'''			,		split='''test'''   )

     lowercase :       Dict       =			Image.open(ds[0]['''file''']   )
     lowercase :       Union[str, Any]       =			Image.open(ds[1]['''file''']   )
     lowercase :       Optional[Any]       =			Image.open(ds[2]['''file''']   )
     lowercase :       List[Any]       =			Image.open(ds[3]['''file''']   )

     return [imagea, imagea], [mapa, mapa]



@require_torch
@require_vision
class 						_A (  _lowerCamelCase      ,       unittest.TestCase				):
  _UpperCamelCase       :      Optional[int]			=      BeitImageProcessor if is_vision_available() else None






  def  __a      (  self		:     List[Any]				)		->   Any:






       """simple docstring"""







       lowercase :       List[Any]       =			BeitImageProcessingTester(self				)






  @property
  def  __a      (  self		:     int				)		->   List[str]:






       """simple docstring"""







       return self.image_processor_tester.prepare_image_processor_dict()






  def  __a      (  self		:     Optional[int]				)		->   int:






       """simple docstring"""







       lowercase :       Tuple       =			self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(_A				,						'''do_resize'''				)				)
       self.assertTrue(hasattr(_A				,						'''size'''				)				)
       self.assertTrue(hasattr(_A				,						'''do_center_crop'''				)				)
       self.assertTrue(hasattr(_A				,						'''center_crop'''				)				)
       self.assertTrue(hasattr(_A				,						'''do_normalize'''				)				)
       self.assertTrue(hasattr(_A				,						'''image_mean'''				)				)
       self.assertTrue(hasattr(_A				,						'''image_std'''				)				)






  def  __a      (  self		:     int				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			self.image_processing_class.from_dict(self.image_processor_dict				)
       self.assertEqual(image_processor.size				,						{'''height''': 20, '''width''': 20}				)
       self.assertEqual(image_processor.crop_size				,						{'''height''': 18, '''width''': 18}				)
       self.assertEqual(image_processor.do_reduce_labels				,						_A				)

       lowercase :       int       =			self.image_processing_class.from_dict(
           self.image_processor_dict				,						size=42				,						crop_size=84				,						reduce_labels=_A				)
       self.assertEqual(image_processor.size				,						{'''height''': 42, '''width''': 42}				)
       self.assertEqual(image_processor.crop_size				,						{'''height''': 84, '''width''': 84}				)
       self.assertEqual(image_processor.do_reduce_labels				,						_A				)






  def  __a      (  self		:     Any				)		->   Union[str, Any]:






       """simple docstring"""







       pass






  def  __a      (  self		:     Union[str, Any]				)		->   int:






       """simple docstring"""







       lowercase :       str       =			self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       lowercase :       Optional[Any]       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_A				)
       for image in image_inputs:
            self.assertIsInstance(_A				,						Image.Image				)

       # Test not batched input
       lowercase :       str       =			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
       lowercase :       int       =			image_processing(_A				,						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  __a      (  self		:     Tuple				)		->   int:






       """simple docstring"""







       lowercase :       int       =			self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       lowercase :       Optional[int]       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_A				,						numpify=_A				)
       for image in image_inputs:
            self.assertIsInstance(_A				,						np.ndarray				)

       # Test not batched input
       lowercase :       List[Any]       =			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
       lowercase :       List[str]       =			image_processing(_A				,						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  __a      (  self		:     str				)		->   Dict:






       """simple docstring"""







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

       # Test not batched input
       lowercase :       List[Any]       =			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
       lowercase :       Tuple       =			image_processing(_A				,						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  __a      (  self		:     Optional[int]				)		->   Tuple:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       lowercase :       Optional[int]       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_A				,						torchify=_A				)
       lowercase :       Union[str, Any]       =			[]
       for image in image_inputs:
            self.assertIsInstance(_A				,						torch.Tensor				)
            maps.append(torch.zeros(image.shape[-2:]				).long()				)

       # Test not batched input
       lowercase :       int       =			image_processing(image_inputs[0]				,						maps[0]				,						return_tensors='''pt'''				)
       self.assertEqual(
           encoding['''pixel_values'''].shape				,						(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )				,						)
       self.assertEqual(
           encoding['''labels'''].shape				,						(
               1,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )				,						)
       self.assertEqual(encoding['''labels'''].dtype				,						torch.long				)
       self.assertTrue(encoding['''labels'''].min().item() >= 0				)
       self.assertTrue(encoding['''labels'''].max().item() <= 255				)

       # Test batched
       lowercase :       int       =			image_processing(_A				,						_A				,						return_tensors='''pt'''				)
       self.assertEqual(
           encoding['''pixel_values'''].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'''],
           )				,						)
       self.assertEqual(
           encoding['''labels'''].shape				,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )				,						)
       self.assertEqual(encoding['''labels'''].dtype				,						torch.long				)
       self.assertTrue(encoding['''labels'''].min().item() >= 0				)
       self.assertTrue(encoding['''labels'''].max().item() <= 255				)

       # Test not batched input (PIL images)
       lowercase      ,	lowercase :       Optional[Any]       =			prepare_semantic_single_inputs()

       lowercase :       str       =			image_processing(_A				,						_A				,						return_tensors='''pt'''				)
       self.assertEqual(
           encoding['''pixel_values'''].shape				,						(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )				,						)
       self.assertEqual(
           encoding['''labels'''].shape				,						(
               1,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )				,						)
       self.assertEqual(encoding['''labels'''].dtype				,						torch.long				)
       self.assertTrue(encoding['''labels'''].min().item() >= 0				)
       self.assertTrue(encoding['''labels'''].max().item() <= 255				)

       # Test batched input (PIL images)
       lowercase      ,	lowercase :       Dict       =			prepare_semantic_batch_inputs()

       lowercase :       Union[str, Any]       =			image_processing(_A				,						_A				,						return_tensors='''pt'''				)
       self.assertEqual(
           encoding['''pixel_values'''].shape				,						(
               2,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )				,						)
       self.assertEqual(
           encoding['''labels'''].shape				,						(
               2,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )				,						)
       self.assertEqual(encoding['''labels'''].dtype				,						torch.long				)
       self.assertTrue(encoding['''labels'''].min().item() >= 0				)
       self.assertTrue(encoding['''labels'''].max().item() <= 255				)






  def  __a      (  self		:     List[Any]				)		->   List[str]:






       """simple docstring"""







       lowercase :       Dict       =			self.image_processing_class(**self.image_processor_dict				)

       # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
       lowercase      ,	lowercase :       str       =			prepare_semantic_single_inputs()
       lowercase :       int       =			image_processing(_A				,						_A				,						return_tensors='''pt'''				)
       self.assertTrue(encoding['''labels'''].min().item() >= 0				)
       self.assertTrue(encoding['''labels'''].max().item() <= 150				)

       lowercase :       Tuple       =			True
       lowercase :       Union[str, Any]       =			image_processing(_A				,						_A				,						return_tensors='''pt'''				)
       self.assertTrue(encoding['''labels'''].min().item() >= 0				)
       self.assertTrue(encoding['''labels'''].max().item() <= 255				)





from __future__ import annotations

import time

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 						_A :






  def __init__(  self		:     List[Any]				,						_A		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     Node | None				)		->   str:






       """simple docstring"""







       lowercase :       Any       =			pos_x
       lowercase :       Union[str, Any]       =			pos_y
       lowercase :       int       =			(pos_y, pos_x)
       lowercase :       List[str]       =			goal_x
       lowercase :       Optional[int]       =			goal_y
       lowercase :       Union[str, Any]       =			parent


class 						_A :






  def __init__(  self		:     Optional[Any]				,						_A		:     tuple[int, int]				,						_A		:     tuple[int, int]				)		->   List[Any]:






       """simple docstring"""







       lowercase :       int       =			Node(start[1]				,						start[0]				,						goal[1]				,						goal[0]				,						_A				)
       lowercase :       int       =			Node(goal[1]				,						goal[0]				,						goal[1]				,						goal[0]				,						_A				)

       lowercase :       List[str]       =			[self.start]
       lowercase :       Optional[int]       =			False






  def  __a      (  self		:     Dict				)		->   Path | None:






       """simple docstring"""







       while self.node_queue:
            lowercase :       Optional[Any]       =			self.node_queue.pop(0				)

            if current_node.pos == self.target.pos:
                 lowercase :       int       =			True
                 return self.retrace_path(_A				)

            lowercase :       Union[str, Any]       =			self.get_successors(_A				)

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

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






  def  __a      (  self		:     str				,						_A		:     Node				)		->   list[Node]:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			[]
       for action in delta:
            lowercase :       List[str]       =			parent.pos_x + action[1]
            lowercase :       str       =			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  __a      (  self		:     Any				,						_A		:     Node | None				)		->   Path:






       """simple docstring"""







       lowercase :       Optional[int]       =			node
       lowercase :       Dict       =			[]
       while current_node is not None:
            path.append((current_node.pos_y, current_node.pos_x)				)
            lowercase :       Tuple       =			current_node.parent
       path.reverse()
       return path


class 						_A :






  def __init__(  self		:     Optional[int]				,						_A		:     List[str]				,						_A		:     List[str]				)		->   int:






       """simple docstring"""







       lowercase :       str       =			BreadthFirstSearch(_A				,						_A				)
       lowercase :       Optional[Any]       =			BreadthFirstSearch(_A				,						_A				)
       lowercase :       List[str]       =			False






  def  __a      (  self		:     Tuple				)		->   Path | None:






       """simple docstring"""







       while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
            lowercase :       str       =			self.fwd_bfs.node_queue.pop(0				)
            lowercase :       Tuple       =			self.bwd_bfs.node_queue.pop(0				)

            if current_bwd_node.pos == current_fwd_node.pos:
                 lowercase :       Optional[Any]       =			True
                 return self.retrace_bidirectional_path(
                     _A				,						_A				)

            lowercase :       Optional[int]       =			current_bwd_node
            lowercase :       Optional[int]       =			current_fwd_node

            lowercase :       Union[str, Any]       =			{
                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  __a      (  self		:     List[Any]				,						_A		:     Node				,						_A		:     Node				)		->   Path:






       """simple docstring"""







       lowercase :       Optional[Any]       =			self.fwd_bfs.retrace_path(_A				)
       lowercase :       Union[str, Any]       =			self.bwd_bfs.retrace_path(_A				)
       bwd_path.pop()
       bwd_path.reverse()
       lowercase :       Optional[int]       =			fwd_path + bwd_path
       return path


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

 doctest.testmod()
 lowerCAmelCase_						=			(0, 0)
 lowerCAmelCase_						=			(len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 lowerCAmelCase_						=			time.time()
 lowerCAmelCase_						=			BreadthFirstSearch(init, goal)
 lowerCAmelCase_						=			bfs.search()
 lowerCAmelCase_						=			time.time() - start_bfs_time

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

 lowerCAmelCase_						=			time.time()
 lowerCAmelCase_						=			BidirectionalBreadthFirstSearch(init, goal)
 lowerCAmelCase_						=			bd_bfs.search()
 lowerCAmelCase_						=			time.time() - start_bd_bfs_time

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