


import heapq
import sys

import numpy as np

A      :    Optional[int]		=					tuple[int, int]







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  []
             lowerCamelCase__		:				Union[str, Any]    =  set()





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             if not self.empty():
                   return self.elements[0][0]
             else:
                   return float("inf"       )





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             return len(self.elements       ) == 0





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             if item not in self.set:
                   heapq.heappush(self.elements					,      (priority, item)       )
                   self.set.add(__lowerCamelCase       )
             else:
                   # update
                   # print("update", item)
                   lowerCamelCase__		:				Optional[Any]    =  []
                   ((lowerCamelCase__)    ,   (lowerCamelCase__))		:				Dict    =  heapq.heappop(self.elements       )
                   while x != item:
                         temp.append((pri, x)       )
                         ((lowerCamelCase__)    ,   (lowerCamelCase__))		:				Any    =  heapq.heappop(self.elements       )
                   temp.append((priority, item)       )
                   for pro, xxx in temp:
                         heapq.heappush(self.elements					,      (pro, xxx)       )





       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			Union[str, Any]       ):


             '''simple docstring'''



             if item in self.set:
                   self.set.remove(__lowerCamelCase       )
                   lowerCamelCase__		:				Tuple    =  []
                   ((lowerCamelCase__)    ,   (lowerCamelCase__))		:				Union[str, Any]    =  heapq.heappop(self.elements       )
                   while x != item:
                         temp.append((pro, x)       )
                         ((lowerCamelCase__)    ,   (lowerCamelCase__))		:				Tuple    =  heapq.heappop(self.elements       )
                   for prito, yyy in temp:
                         heapq.heappush(self.elements					,      (prito, yyy)       )





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             return self.elements[0][1]





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             ((lowerCamelCase__)    ,   (lowerCamelCase__))		:				Any    =  heapq.heappop(self.elements       )
             self.set.remove(__lowerCamelCase       )
             return (priority, item)



def       lowercase_		(		_A			:    TPos    ,					_A			:    TPos						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  np.array(_A						)
      lowerCamelCase__		:				Union[str, Any]    =  np.array(_A						)
      return np.linalg.norm(a - b						)

def       lowercase_		(		_A			:    TPos    ,					_A			:    TPos						):

      """simple docstring"""




      return consistent_heuristic(_A    ,					_A						) // t

def       lowercase_		(		_A			:    TPos    ,					_A			:    TPos						):

      """simple docstring"""




      return abs(p[0] - goal[0]						) + abs(p[1] - goal[1]						)

def       lowercase_		(		_A			:    TPos    ,					_A			:    int    ,					_A			:    TPos    ,					_A			:    dict[TPos, float]						):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  g_function[start] + Wa * heuristics[i](_A    ,					_A						)
      return ans

def       lowercase_		(		_A			:    str    ,					_A			:    Tuple    ,					_A			:    List[Any]						):

      """simple docstring"""




      lowerCamelCase__		:				str    =  np.chararray((n, n)						)
      for i in range(_A						):
            for j in range(_A						):
                  lowerCamelCase__		:				Tuple    =  "*"

      for i in range(_A						):
            for j in range(_A						):
                  if (j, (n - 1) - i) in blocks:
                        lowerCamelCase__		:				int    =  "#"

      lowerCamelCase__		:				Tuple    =  "-"
      lowerCamelCase__		:				List[Any]    =  back_pointer[goal]
      while x != start:
            ((lowerCamelCase__)    ,   (lowerCamelCase__))		:				Union[str, Any]    =  x
            # print(x)
            lowerCamelCase__		:				Optional[int]    =  "-"
            lowerCamelCase__		:				List[str]    =  back_pointer[x]
      lowerCamelCase__		:				Tuple    =  "-"

      for i in range(_A						):
            for j in range(_A						):
                  if (i, j) == (0, n - 1):
                        print(grid[i][j]    ,					end=" "						)
                        print("<-- End position"    ,					end=" "						)
                  else:
                        print(grid[i][j]    ,					end=" "						)
            print()
      print("^"						)
      print("Start position"						)
      print()
      print("# is an obstacle"						)
      print("- is the path taken by algorithm"						)
      print("PATH TAKEN BY THE ALGORITHM IS:-"						)
      lowerCamelCase__		:				Optional[int]    =  back_pointer[goal]
      while x != start:
            print(_A    ,					end=" "						)
            lowerCamelCase__		:				int    =  back_pointer[x]
      print(_A						)
      sys.exit()

def       lowercase_		(		_A			:    TPos						):

      """simple docstring"""




      if p[0] < 0 or p[0] > n - 1:
            return False
      if p[1] < 0 or p[1] > n - 1:
            return False
      return True

def       lowercase_		(		_A			:    List[str]    ,					_A			:    List[Any]    ,					_A			:    Dict    ,					_A			:    Tuple    ,					_A			:    List[str]    ,					_A			:    List[Any]    ,					_A			:    Optional[int]    ,					_A			:    int    ,					):

      """simple docstring"""




      for itera in range(_A						):
            open_list[itera].remove_element(_A						)
      # print("s", s)
      # print("j", j)
      ((lowerCamelCase__)    ,   (lowerCamelCase__))		:				Optional[Any]    =  s
      lowerCamelCase__		:				Dict    =  (x - 1, y)
      lowerCamelCase__		:				Tuple    =  (x + 1, y)
      lowerCamelCase__		:				List[str]    =  (x, y + 1)
      lowerCamelCase__		:				List[str]    =  (x, y - 1)

      for neighbours in [left, right, up, down]:
            if neighbours not in blocks:
                  if valid(_A						) and neighbours not in visited:
                        # print("neighbour", neighbours)
                        visited.add(_A						)
                        lowerCamelCase__		:				Dict    =  -1
                        lowerCamelCase__		:				Dict    =  float("inf"						)

                  if valid(_A						) and g_function[neighbours] > g_function[s] + 1:
                        lowerCamelCase__		:				Any    =  g_function[s] + 1
                        lowerCamelCase__		:				Any    =  s
                        if neighbours not in close_list_anchor:
                              open_list[0].put(_A    ,					key(_A    ,					0    ,					_A    ,					_A						)						)
                              if neighbours not in close_list_inad:
                                    for var in range(1    ,					_A						):
                                          if key(_A    ,					_A    ,					_A    ,					_A						) <= Wa * key(
                                              _A    ,					0    ,					_A    ,					_A						):
                                                open_list[j].put(
                                                    _A    ,					key(_A    ,					_A    ,					_A    ,					_A						)						)

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  []
      for x in range(1    ,					5						):
            for y in range(1    ,					6						):
                  some_list.append((x, y)						)

      for x in range(15    ,					20						):
            some_list.append((x, 17)						)

      for x in range(10    ,					19						):
            for y in range(1    ,					15						):
                  some_list.append((x, y)						)

    # L block
      for x in range(1    ,					4						):
            for y in range(12    ,					19						):
                  some_list.append((x, y)						)
      for x in range(3    ,					13						):
            for y in range(16    ,					19						):
                  some_list.append((x, y)						)
      return some_list


A      :    Optional[Any]		=					{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

A      :    Optional[Any]		=					[
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
A      :    str		=					make_common_ground()


A      :    Any		=					blocks_blk
# hyper parameters
A      :    Optional[Any]		=					1
A      :    List[str]		=					1
A      :    Union[str, Any]		=					20
A      :    Dict		=					3  # one consistent and two other inconsistent

# start and end destination
A      :    Any		=					(0, 0)
A      :    Optional[Any]		=					(n - 1, n - 1)

A      :    str		=					1

def       lowercase_		(		_A			:    TPos    ,					_A			:    TPos    ,					_A			:    int						):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  {start: 0, goal: float("inf"						)}
      lowerCamelCase__		:				Tuple    =  {start: -1, goal: -1}
      lowerCamelCase__		:				List[Any]    =  []
      lowerCamelCase__		:				List[Any]    =  set()

      for i in range(_A						):
            open_list.append(PriorityQueue()						)
            open_list[i].put(_A    ,					key(_A    ,					_A    ,					_A    ,					_A						)						)

      lowerCamelCase__		:				list[int]    =  []
      lowerCamelCase__		:				list[int]    =  []
      while open_list[0].minkey() < float("inf"						):
            for i in range(1    ,					_A						):
                  # print(open_list[0].minkey(), open_list[i].minkey())
                  if open_list[i].minkey() <= Wa * open_list[0].minkey():
                        global t
                        t += 1
                        if g_function[goal] <= open_list[i].minkey():
                              if g_function[goal] < float("inf"						):
                                    do_something(_A    ,					_A    ,					_A						)
                        else:
                              lowerCamelCase__    ,   lowerCamelCase__		:				Optional[int]    =  open_list[i].top_show()
                              visited.add(_A						)
                              expand_state(
                                  _A    ,					_A    ,					_A    ,					_A    ,					_A    ,					_A    ,					_A    ,					_A    ,					)
                              close_list_inad.append(_A						)
                  else:
                        if g_function[goal] <= open_list[0].minkey():
                              if g_function[goal] < float("inf"						):
                                    do_something(_A    ,					_A    ,					_A						)
                        else:
                              lowerCamelCase__		:				List[Any]    =  open_list[0].top_show()
                              visited.add(_A						)
                              expand_state(
                                  _A    ,					0    ,					_A    ,					_A    ,					_A    ,					_A    ,					_A    ,					_A    ,					)
                              close_list_anchor.append(_A						)
      print("No path found to goal"						)
      print()
      for i in range(n - 1    ,					-1    ,					-1						):
            for j in range(_A						):
                  if (j, i) in blocks:
                        print("#"    ,					end=" "						)
                  elif (j, i) in back_pointer:
                        if (j, i) == (n - 1, n - 1):
                              print("*"    ,					end=" "						)
                        else:
                              print("-"    ,					end=" "						)
                  else:
                        print("*"    ,					end=" "						)
                  if (j, i) == (n - 1, n - 1):
                        print("<-- End position"    ,					end=" "						)
            print()
      print("^"						)
      print("Start position"						)
      print()
      print("# is an obstacle"						)
      print("- is the path taken by algorithm"						)


if __name__ == "__main__":
    multi_a_star(start, goal, n_heuristic)






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 DetaImageProcessor







class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       def __init__(   self							:			Any					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			List[Any]=7					,      __lowerCamelCase							:			Dict=3					,      __lowerCamelCase							:			Union[str, Any]=30					,      __lowerCamelCase							:			Union[str, Any]=400					,      __lowerCamelCase							:			Dict=True					,      __lowerCamelCase							:			Tuple=None					,      __lowerCamelCase							:			str=True					,      __lowerCamelCase							:			List[str]=[0.5, 0.5, 0.5]					,      __lowerCamelCase							:			str=[0.5, 0.5, 0.5]					,      __lowerCamelCase							:			Any=True					,      __lowerCamelCase							:			Union[str, Any]=1 / 255					,      __lowerCamelCase							:			Optional[Any]=True					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
             lowerCamelCase__		:				Dict    =  parent
             lowerCamelCase__		:				Optional[int]    =  batch_size
             lowerCamelCase__		:				str    =  num_channels
             lowerCamelCase__		:				Optional[Any]    =  min_resolution
             lowerCamelCase__		:				List[Any]    =  max_resolution
             lowerCamelCase__		:				int    =  do_resize
             lowerCamelCase__		:				Union[str, Any]    =  size
             lowerCamelCase__		:				Union[str, Any]    =  do_normalize
             lowerCamelCase__		:				int    =  image_mean
             lowerCamelCase__		:				Optional[int]    =  image_std
             lowerCamelCase__		:				List[Any]    =  do_rescale
             lowerCamelCase__		:				Optional[Any]    =  rescale_factor
             lowerCamelCase__		:				Any    =  do_pad





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             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 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			List[str]=False       ):


             '''simple docstring'''



             if not batched:
                   lowerCamelCase__		:				Tuple    =  image_inputs[0]
                   if isinstance(__lowerCamelCase					,      Image.Image       ):
                         lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  image.size
                   else:
                         lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  image.shape[1], image.shape[2]
                   if w < h:
                         lowerCamelCase__		:				List[Any]    =  int(self.size["shortest_edge"] * h / w       )
                         lowerCamelCase__		:				Optional[Any]    =  self.size["shortest_edge"]
                   elif w > h:
                         lowerCamelCase__		:				List[Any]    =  self.size["shortest_edge"]
                         lowerCamelCase__		:				List[str]    =  int(self.size["shortest_edge"] * w / h       )
                   else:
                         lowerCamelCase__		:				Optional[int]    =  self.size["shortest_edge"]
                         lowerCamelCase__		:				Union[str, Any]    =  self.size["shortest_edge"]

             else:
                   lowerCamelCase__		:				Dict    =  []
                   for image in image_inputs:
                         lowerCamelCase__    ,   lowerCamelCase__		:				Union[str, Any]    =  self.get_expected_values([image]       )
                         expected_values.append((expected_height, expected_width)       )
                   lowerCamelCase__		:				str    =  max(__lowerCamelCase					,      key=lambda __lowerCamelCase       : item[0]       )[0]
                   lowerCamelCase__		:				Optional[Any]    =  max(__lowerCamelCase					,      key=lambda __lowerCamelCase       : item[1]       )[1]

             return expected_height, expected_width







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


       """simple docstring"""




       A__											=			DetaImageProcessor if is_vision_available() else None





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  DetaImageProcessingTester(self       )





       @property
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  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 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size					,      {"shortest_edge": 18, "longest_edge": 1333}       )
             self.assertEqual(image_processor.do_pad					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



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

             # Test not batched input
             lowerCamelCase__		:				Optional[int]    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Optional[Any]    =  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
             lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  self.image_processor_tester.get_expected_values(__lowerCamelCase					,      batched=__lowerCamelCase       )

             lowerCamelCase__		:				List[Any]    =  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 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             lowerCamelCase__		:				Optional[int]    =  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
             lowerCamelCase__		:				Any    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  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
             lowerCamelCase__		:				Optional[Any]    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Union[str, Any]    =  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 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             lowerCamelCase__		:				Dict    =  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
             lowerCamelCase__		:				Dict    =  image_processing(image_inputs[0]					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  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
             lowerCamelCase__		:				Union[str, Any]    =  image_processing(__lowerCamelCase					,      return_tensors="pt"       ).pixel_values

             lowerCamelCase__    ,   lowerCamelCase__		:				Dict    =  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 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
             with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"					,      "r"       ) as f:
                   lowerCamelCase__		:				Dict    =  json.loads(f.read()       )

             lowerCamelCase__		:				Any    =  {"image_id": 39769, "annotations": target}

             # encode them
             lowerCamelCase__		:				Union[str, Any]    =  DetaImageProcessor()
             lowerCamelCase__		:				List[str]    =  image_processing(images=__lowerCamelCase					,      annotations=__lowerCamelCase					,      return_tensors="pt"       )

             # verify pixel values
             lowerCamelCase__		:				List[str]    =  torch.Size([1, 3, 800, 1066]       )
             self.assertEqual(encoding["pixel_values"].shape					,      __lowerCamelCase       )

             lowerCamelCase__		:				Union[str, Any]    =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
             self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]					,      __lowerCamelCase					,      atol=1E-4       )       )

             # verify area
             lowerCamelCase__		:				Tuple    =  torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,      __lowerCamelCase       )       )
             # verify boxes
             lowerCamelCase__		:				Union[str, Any]    =  torch.Size([6, 4]       )
             self.assertEqual(encoding["labels"][0]["boxes"].shape					,      __lowerCamelCase       )
             lowerCamelCase__		:				List[Any]    =  torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,      __lowerCamelCase					,      atol=1E-3       )       )
             # verify image_id
             lowerCamelCase__		:				Optional[Any]    =  torch.tensor([39769]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,      __lowerCamelCase       )       )
             # verify is_crowd
             lowerCamelCase__		:				Dict    =  torch.tensor([0, 0, 0, 0, 0, 0]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,      __lowerCamelCase       )       )
             # verify class_labels
             lowerCamelCase__		:				int    =  torch.tensor([75, 75, 63, 65, 17, 17]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,      __lowerCamelCase       )       )
             # verify orig_size
             lowerCamelCase__		:				int    =  torch.tensor([480, 640]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,      __lowerCamelCase       )       )
             # verify size
             lowerCamelCase__		:				Optional[int]    =  torch.tensor([800, 1066]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,      __lowerCamelCase       )       )





       @slow
       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
             with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"					,      "r"       ) as f:
                   lowerCamelCase__		:				Tuple    =  json.loads(f.read()       )

             lowerCamelCase__		:				List[str]    =  {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

             lowerCamelCase__		:				Union[str, Any]    =  pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"       )

             # encode them
             lowerCamelCase__		:				Tuple    =  DetaImageProcessor(format="coco_panoptic"       )
             lowerCamelCase__		:				Dict    =  image_processing(images=__lowerCamelCase					,      annotations=__lowerCamelCase					,      masks_path=__lowerCamelCase					,      return_tensors="pt"       )

             # verify pixel values
             lowerCamelCase__		:				List[Any]    =  torch.Size([1, 3, 800, 1066]       )
             self.assertEqual(encoding["pixel_values"].shape					,      __lowerCamelCase       )

             lowerCamelCase__		:				Dict    =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
             self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]					,      __lowerCamelCase					,      atol=1E-4       )       )

             # verify area
             lowerCamelCase__		:				List[Any]    =  torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,      __lowerCamelCase       )       )
             # verify boxes
             lowerCamelCase__		:				Any    =  torch.Size([6, 4]       )
             self.assertEqual(encoding["labels"][0]["boxes"].shape					,      __lowerCamelCase       )
             lowerCamelCase__		:				int    =  torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,      __lowerCamelCase					,      atol=1E-3       )       )
             # verify image_id
             lowerCamelCase__		:				Optional[int]    =  torch.tensor([39769]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,      __lowerCamelCase       )       )
             # verify is_crowd
             lowerCamelCase__		:				Dict    =  torch.tensor([0, 0, 0, 0, 0, 0]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,      __lowerCamelCase       )       )
             # verify class_labels
             lowerCamelCase__		:				Tuple    =  torch.tensor([17, 17, 63, 75, 75, 93]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,      __lowerCamelCase       )       )
             # verify masks
             lowerCamelCase__		:				Union[str, Any]    =  822873
             self.assertEqual(encoding["labels"][0]["masks"].sum().item()					,      __lowerCamelCase       )
             # verify orig_size
             lowerCamelCase__		:				Optional[Any]    =  torch.tensor([480, 640]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,      __lowerCamelCase       )       )
             # verify size
             lowerCamelCase__		:				Union[str, Any]    =  torch.tensor([800, 1066]       )
             self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,      __lowerCamelCase       )       )


