





'''simple docstring'''

from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
lowercase__		:     Union[str, Any]			=							0

lowercase__		:     Optional[Any]			=							[
    [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],
]

lowercase__		:     Optional[int]			=							[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

lowercase__		:     Any			=							tuple[int, int]
class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						Optional[int]			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						Node | None			,					)       ->	None:

            '''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()
            _UpperCamelCase							       =			self.g_cost + self.h_cost





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

            '''simple docstring'''




            _UpperCamelCase							       =			self.pos_x - self.goal_x
            _UpperCamelCase							       =			self.pos_y - self.goal_y
            if HEURISTIC == 1:
                 return abs(lowerCAmelCase__	) + abs(lowerCAmelCase__	)
            else:
                 return sqrt(dy**2 + dx**2	)







       def __lt__(				self  :						str			,					lowerCAmelCase__  :						Node	)       ->	bool:

            '''simple docstring'''




            return self.f_cost < other.f_cost

class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						Any			,					lowerCAmelCase__  :						TPosition			,					lowerCAmelCase__  :						TPosition	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			Node(start[1]			,					start[0]			,					goal[1]			,					goal[0]			,					0			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			Node(goal[1]			,					goal[0]			,					goal[1]			,					goal[0]			,					99999			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			[self.start]
            _UpperCamelCase							       =			[]

            _UpperCamelCase							       =			False





       def snake_case__		(				self  :						List[Any]	)       ->	list[TPosition]:

            '''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:
                      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__	)

            return [self.start.pos]





       def snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Node	)       ->	list[Node]:

            '''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 snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						Node | None	)       ->	list[TPosition]:

            '''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

class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						List[str]			,					lowerCAmelCase__  :						TPosition			,					lowerCAmelCase__  :						TPosition	)       ->	None:

            '''simple docstring'''




            _UpperCamelCase							       =			AStar(lowerCAmelCase__			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			AStar(lowerCAmelCase__			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			False





       def snake_case__		(				self  :						int	)       ->	list[TPosition]:

            '''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							       =			self.fwd_astar.open_nodes.pop(0	)
                 _UpperCamelCase							       =			self.bwd_astar.open_nodes.pop(0	)

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

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

                 _UpperCamelCase							       =			current_bwd_node
                 _UpperCamelCase							       =			current_fwd_node

                 _UpperCamelCase							       =			{
                     self.fwd_astar: self.fwd_astar.get_successors(lowerCAmelCase__	),
                     self.bwd_astar: self.bwd_astar.get_successors(lowerCAmelCase__	),
                 }

                 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(lowerCAmelCase__	)
                           else:
                                # retrieve the best current path
                                _UpperCamelCase							       =			astar.open_nodes.pop(
                                    astar.open_nodes.index(lowerCAmelCase__	)	)

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

            return [self.fwd_astar.start.pos]







       def snake_case__		(				self  :						List[str]			,					lowerCAmelCase__  :						Node			,					lowerCAmelCase__  :						Node	)       ->	list[TPosition]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.fwd_astar.retrace_path(lowerCAmelCase__	)
            _UpperCamelCase							       =			self.bwd_astar.retrace_path(lowerCAmelCase__	)
            bwd_path.pop()
            bwd_path.reverse()
            _UpperCamelCase							       =			fwd_path + bwd_path
            return path


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

   lowercase__		:     Any			=							time.time()
   lowercase__		:     Union[str, Any]			=							AStar(init, goal)
   lowercase__		:     Optional[Any]			=							a_star.search()
   lowercase__		:     List[Any]			=							time.time() - start_time
   print(F"""AStar execution time = {end_time:f} seconds""")

   lowercase__		:     Tuple			=							time.time()
   lowercase__		:     List[str]			=							BidirectionalAStar(init, goal)
   lowercase__		:     List[Any]			=							time.time() - bd_start_time
   print(F"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")








import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor


class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Dict=7			,						__lowerCAmelCase					:					Tuple=3			,						__lowerCAmelCase					:					int=30			,						__lowerCAmelCase					:					Dict=4_00			,						__lowerCAmelCase					:					Optional[Any]=True			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Dict=True			,						__lowerCAmelCase					:					Optional[Any]=[0.5, 0.5, 0.5]			,						__lowerCAmelCase					:					Dict=[0.5, 0.5, 0.5]			,						__lowerCAmelCase					:					List[str]=True			,						__lowerCAmelCase					:					List[str]=1 / 2_55			,						__lowerCAmelCase					:					int=True			,						)   ->	List[str]:
    # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
    _A      =       size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
    _A      =       parent
    _A      =       batch_size
    _A      =       num_channels
    _A      =       min_resolution
    _A      =       max_resolution
    _A      =       do_resize
    _A      =       size
    _A      =       do_normalize
    _A      =       image_mean
    _A      =       image_std
    _A      =       do_rescale
    _A      =       rescale_factor
    _A      =       do_pad

 def     snake_case_      (    self					:					Optional[int]      )   ->	Any:
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_rescale": self.do_rescale,
        "rescale_factor": self.rescale_factor,
        "do_pad": self.do_pad,
    }

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					str=False      )   ->	Dict:
    if not batched:
       _A      =       image_inputs[0]
       if isinstance(__lowerCAmelCase			,						Image.Image      ):
          _A  ,    _A      =       image.size
       else:
          _A  ,    _A      =       image.shape[1], image.shape[2]
       if w < h:
          _A      =       int(self.size['''shortest_edge'''] * h / w      )
          _A      =       self.size['''shortest_edge''']
       elif w > h:
          _A      =       self.size['''shortest_edge''']
          _A      =       int(self.size['''shortest_edge'''] * w / h      )
       else:
          _A      =       self.size['''shortest_edge''']
          _A      =       self.size['''shortest_edge''']

    else:
       _A      =       []
       for image in image_inputs:
          _A  ,    _A      =       self.get_expected_values([image]      )
          expected_values.append((expected_height, expected_width)      )
       _A      =       max(__lowerCAmelCase			,						key=lambda __lowerCAmelCase      : item[0]      )[0]
       _A      =       max(__lowerCAmelCase			,						key=lambda __lowerCAmelCase      : item[1]      )[1]

    return expected_height, expected_width


@require_torch
@require_vision
class 	lowerCamelCase__	(       _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: Any =  DeformableDetrImageProcessor if is_vision_available() else None

 def     snake_case_      (    self					:					Optional[int]      )   ->	Any:
    _A      =       DeformableDetrImageProcessingTester(self      )

 @property
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Dict:
    return self.image_processor_tester.prepare_image_processor_dict()

 def     snake_case_      (    self					:					Optional[int]      )   ->	List[str]:
    _A      =       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_rescale'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_pad'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''size'''      )      )

 def     snake_case_      (    self					:					List[str]      )   ->	int:
    _A      =       self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size			,						{'''shortest_edge''': 18, '''longest_edge''': 13_33}      )
    self.assertEqual(image_processor.do_pad			,						__lowerCAmelCase      )

    _A      =       self.image_processing_class.from_dict(
        self.image_processor_dict			,						size=42			,						max_size=84			,						pad_and_return_pixel_mask=__lowerCAmelCase      )
    self.assertEqual(image_processor.size			,						{'''shortest_edge''': 42, '''longest_edge''': 84}      )
    self.assertEqual(image_processor.do_pad			,						__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					List[str]      )   ->	Optional[int]:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random PIL images
    _A      =       prepare_image_inputs(self.image_processor_tester			,						equal_resolution=__lowerCAmelCase      )
    for image in image_inputs:
       self.assertIsInstance(__lowerCAmelCase			,						Image.Image      )

    # Test not batched input
    _A      =       image_processing(image_inputs[0]			,						return_tensors='''pt'''      ).pixel_values

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase      )

    self.assertEqual(
        encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

    # Test batched
    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase			,						batched=__lowerCAmelCase      )

    _A      =       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,
            expected_height,
            expected_width,
        )			,						)

 def     snake_case_      (    self					:					Tuple      )   ->	int:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random numpy tensors
    _A      =       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
    _A      =       image_processing(image_inputs[0]			,						return_tensors='''pt'''      ).pixel_values

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase      )

    self.assertEqual(
        encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

    # Test batched
    _A      =       image_processing(__lowerCAmelCase			,						return_tensors='''pt'''      ).pixel_values

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase			,						batched=__lowerCAmelCase      )

    self.assertEqual(
        encoded_images.shape			,						(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )			,						)

 def     snake_case_      (    self					:					Optional[Any]      )   ->	int:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    _A      =       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
    _A      =       image_processing(image_inputs[0]			,						return_tensors='''pt'''      ).pixel_values

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase      )

    self.assertEqual(
        encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

    # Test batched
    _A      =       image_processing(__lowerCAmelCase			,						return_tensors='''pt'''      ).pixel_values

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase			,						batched=__lowerCAmelCase      )

    self.assertEqual(
        encoded_images.shape			,						(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )			,						)

 @slow
 def     snake_case_      (    self					:					Optional[Any]      )   ->	Optional[int]:
    # prepare image and target
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''			,						'''r'''      ) as f:
       _A      =       json.loads(f.read()      )

    _A      =       {'''image_id''': 3_97_69, '''annotations''': target}

    # encode them
    _A      =       DeformableDetrImageProcessor()
    _A      =       image_processing(images=__lowerCAmelCase			,						annotations=__lowerCAmelCase			,						return_tensors='''pt'''      )

    # verify pixel values
    _A      =       torch.Size([1, 3, 8_00, 10_66]      )
    self.assertEqual(encoding['''pixel_values'''].shape			,						__lowerCAmelCase      )

    _A      =       torch.tensor([0.2796, 0.3138, 0.3481]      )
    self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]			,						__lowerCAmelCase			,						atol=1E-4      )      )

    # verify area
    _A      =       torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,						__lowerCAmelCase      )      )
    # verify boxes
    _A      =       torch.Size([6, 4]      )
    self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,						__lowerCAmelCase      )
    _A      =       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,						__lowerCAmelCase			,						atol=1E-3      )      )
    # verify image_id
    _A      =       torch.tensor([3_97_69]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,						__lowerCAmelCase      )      )
    # verify is_crowd
    _A      =       torch.tensor([0, 0, 0, 0, 0, 0]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,						__lowerCAmelCase      )      )
    # verify class_labels
    _A      =       torch.tensor([75, 75, 63, 65, 17, 17]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,						__lowerCAmelCase      )      )
    # verify orig_size
    _A      =       torch.tensor([4_80, 6_40]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,						__lowerCAmelCase      )      )
    # verify size
    _A      =       torch.tensor([8_00, 10_66]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,						__lowerCAmelCase      )      )

 @slow
 def     snake_case_      (    self					:					List[str]      )   ->	List[str]:
    # prepare image, target and masks_path
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''			,						'''r'''      ) as f:
       _A      =       json.loads(f.read()      )

    _A      =       {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

    _A      =       pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

    # encode them
    _A      =       DeformableDetrImageProcessor(format='''coco_panoptic'''      )
    _A      =       image_processing(images=__lowerCAmelCase			,						annotations=__lowerCAmelCase			,						masks_path=__lowerCAmelCase			,						return_tensors='''pt'''      )

    # verify pixel values
    _A      =       torch.Size([1, 3, 8_00, 10_66]      )
    self.assertEqual(encoding['''pixel_values'''].shape			,						__lowerCAmelCase      )

    _A      =       torch.tensor([0.2796, 0.3138, 0.3481]      )
    self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]			,						__lowerCAmelCase			,						atol=1E-4      )      )

    # verify area
    _A      =       torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,						__lowerCAmelCase      )      )
    # verify boxes
    _A      =       torch.Size([6, 4]      )
    self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,						__lowerCAmelCase      )
    _A      =       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,						__lowerCAmelCase			,						atol=1E-3      )      )
    # verify image_id
    _A      =       torch.tensor([3_97_69]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,						__lowerCAmelCase      )      )
    # verify is_crowd
    _A      =       torch.tensor([0, 0, 0, 0, 0, 0]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,						__lowerCAmelCase      )      )
    # verify class_labels
    _A      =       torch.tensor([17, 17, 63, 75, 75, 93]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,						__lowerCAmelCase      )      )
    # verify masks
    _A      =       82_28_73
    self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()			,						__lowerCAmelCase      )
    # verify orig_size
    _A      =       torch.tensor([4_80, 6_40]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,						__lowerCAmelCase      )      )
    # verify size
    _A      =       torch.tensor([8_00, 10_66]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,						__lowerCAmelCase      )      )



