





import heapq
import sys

import numpy as np

_lowerCamelCase      							=   tuple[int, int]







class       __A							:




      """simple docstring"""







      def __init__(		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		[]
             _lowerCamelCase				:  List[Any]						=		set()


      def 					__snake_case      (		self):



             """simple docstring"""





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


      def 					__snake_case      (		self):



             """simple docstring"""





             return len(self.elements) == 0


      def 					__snake_case      (		self   ,				a__   ,				a__):



             """simple docstring"""





             if item not in self.set:
                    heapq.heappush(self.elements   ,				(priority, item))
                    self.set.add(a__)
             else:
                    # update
                    # print("update", item)
                    _lowerCamelCase				:  str						=		[]
                    ((_lowerCamelCase),	(_lowerCamelCase))				:  int						=		heapq.heappop(self.elements)
                    while x != item:
                           temp.append((pri, x))
                           ((_lowerCamelCase),	(_lowerCamelCase))				:  List[str]						=		heapq.heappop(self.elements)
                    temp.append((priority, item))
                    for pro, xxx in temp:
                           heapq.heappush(self.elements   ,				(pro, xxx))


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





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


      def 					__snake_case      (		self):



             """simple docstring"""





             return self.elements[0][1]



      def 					__snake_case      (		self):



             """simple docstring"""





             ((_lowerCamelCase),	(_lowerCamelCase))				:  List[str]						=		heapq.heappop(self.elements)
             self.set.remove(a__)
             return (priority, item)




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       # euclidean distance
       _lowerCamelCase				:  Union[str, Any]						=		np.array(lowercase_   )
       _lowerCamelCase				:  Any						=		np.array(lowercase_   )
       return np.linalg.norm(a - b   )




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       # integer division by time variable
       return consistent_heuristic(lowercase_			,     lowercase_   ) // t




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       # manhattan distance
       return abs(p[0] - goal[0]   ) + abs(p[1] - goal[1]   )




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  List[Any]						=		g_function[start] + Wa * heuristics[i](lowercase_			,     lowercase_   )
       return ans




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  List[str]						=		np.chararray((n, n)   )
       for i in range(lowercase_   ):
              for j in range(lowercase_   ):
                     _lowerCamelCase				:  Any						=		'''*'''

       for i in range(lowercase_   ):
              for j in range(lowercase_   ):
                     if (j, (n - 1) - i) in blocks:
                            _lowerCamelCase				:  Optional[int]						=		'''#'''

       _lowerCamelCase				:  Dict						=		'''-'''
       _lowerCamelCase				:  str						=		back_pointer[goal]
       while x != start:
              ((_lowerCamelCase),	(_lowerCamelCase))				:  str						=		x
              # print(x)
              _lowerCamelCase				:  List[Any]						=		'''-'''
              _lowerCamelCase				:  Dict						=		back_pointer[x]
       _lowerCamelCase				:  int						=		'''-'''

       for i in range(lowercase_   ):
              for j in range(lowercase_   ):
                     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				:  List[Any]						=		back_pointer[goal]
       while x != start:
              print(lowercase_			,     end=''' '''   )
              _lowerCamelCase				:  Optional[Any]						=		back_pointer[x]
       print(lowercase_   )
       sys.exit()




def  __UpperCAmelCase(   lowercase_   ):
       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  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     ):
       for itera in range(lowercase_   ):
              open_list[itera].remove_element(lowercase_   )
       # print("s", s)
       # print("j", j)
       ((_lowerCamelCase),	(_lowerCamelCase))				:  List[str]						=		s
       _lowerCamelCase				:  Any						=		(x - 1, y)
       _lowerCamelCase				:  Dict						=		(x + 1, y)
       _lowerCamelCase				:  Dict						=		(x, y + 1)
       _lowerCamelCase				:  Any						=		(x, y - 1)

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

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




def  __UpperCAmelCase(   ):
       _lowerCamelCase				:  Union[str, 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


_lowerCamelCase      							=   {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

_lowerCamelCase      							=   [
    (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),
]
_lowerCamelCase      							=   make_common_ground()


_lowerCamelCase      							=   blocks_blk
# hyper parameters
_lowerCamelCase      							=   1
_lowerCamelCase      							=   1
_lowerCamelCase      							=   20
_lowerCamelCase      							=   3  # one consistent and two other inconsistent

# start and end destination
_lowerCamelCase      							=   (0, 0)
_lowerCamelCase      							=   (n - 1, n - 1)

_lowerCamelCase      							=   1




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  Optional[Any]						=		{start: 0, goal: float('''inf'''   )}
       _lowerCamelCase				:  str						=		{start: -1, goal: -1}
       _lowerCamelCase				:  str						=		[]
       _lowerCamelCase				:  Dict						=		set()

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

       _lowerCamelCase				:  list[int]						=		[]
       _lowerCamelCase				:  list[int]						=		[]
       while open_list[0].minkey() < float('''inf'''   ):
              for i in range(1			,     lowercase_   ):
                     # 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(lowercase_			,     lowercase_			,     lowercase_   )
                            else:
                                   _lowerCamelCase,	_lowerCamelCase				:  Optional[int]						=		open_list[i].top_show()
                                   visited.add(lowercase_   )
                                   expand_state(
                                       lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     )
                                   close_list_inad.append(lowercase_   )
                     else:
                            if g_function[goal] <= open_list[0].minkey():
                                   if g_function[goal] < float('''inf'''   ):
                                          do_something(lowercase_			,     lowercase_			,     lowercase_   )
                            else:
                                   _lowerCamelCase				:  Union[str, Any]						=		open_list[0].top_show()
                                   visited.add(lowercase_   )
                                   expand_state(
                                       lowercase_			,     0			,     lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     )
                                   close_list_anchor.append(lowercase_   )
       print('''No path found to goal'''   )
       print()
       for i in range(n - 1			,     -1			,     -1   ):
              for j in range(lowercase_   ):
                     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 unittest

import numpy as np
import requests

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

       from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
       _lowerCamelCase      							=   False

if is_vision_available():
       from PIL import Image

       from transformers import PixaStructImageProcessor







class       __A							(   unittest.TestCase	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__=7   ,				a__=3   ,				a__=18   ,				a__=30   ,				a__=400   ,				a__=None   ,				a__=True   ,				a__=True   ,				a__=None   ,				):



             """simple docstring"""





             _lowerCamelCase				:  str						=		size if size is not None else {'''height''': 20, '''width''': 20}
             _lowerCamelCase				:  int						=		parent
             _lowerCamelCase				:  Tuple						=		batch_size
             _lowerCamelCase				:  Union[str, Any]						=		num_channels
             _lowerCamelCase				:  Tuple						=		image_size
             _lowerCamelCase				:  Dict						=		min_resolution
             _lowerCamelCase				:  int						=		max_resolution
             _lowerCamelCase				:  Dict						=		size
             _lowerCamelCase				:  Dict						=		do_normalize
             _lowerCamelCase				:  List[Any]						=		do_convert_rgb
             _lowerCamelCase				:  Optional[int]						=		[512, 1024, 2048, 4096]
             _lowerCamelCase				:  Optional[int]						=		patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}


      def 					__snake_case      (		self):



             """simple docstring"""





             return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
             _lowerCamelCase				:  Union[str, Any]						=		Image.open(requests.get(a__   ,				stream=a__).raw).convert('''RGB''')
             return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,)
@require_torch
@require_vision
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   PixaStructImageProcessor if is_vision_available() else None


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		PixaStructImageProcessingTester(self)


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self.image_processor_tester.prepare_image_processor_dict()


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		self.image_processing_class(**self.image_processor_dict)
             self.assertTrue(hasattr(a__   ,				'''do_normalize'''))
             self.assertTrue(hasattr(a__   ,				'''do_convert_rgb'''))


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.image_processor_tester.prepare_dummy_image()

             _lowerCamelCase				:  List[Any]						=		self.image_processing_class(**self.image_processor_dict)
             _lowerCamelCase				:  Optional[int]						=		2048

             _lowerCamelCase				:  Any						=		image_processor(a__   ,				return_tensors='''pt'''   ,				max_patches=a__)
             self.assertTrue(torch.allclose(inputs.flattened_patches.mean()   ,				torch.tensor(0.0606)   ,				atol=1e-3   ,				rtol=1e-3))


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		self.image_processing_class(**self.image_processor_dict)
             # create random PIL images
             _lowerCamelCase				:  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
             _lowerCamelCase				:  Optional[Any]						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  Optional[int]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Dict						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)


      def 					__snake_case      (		self):



             """simple docstring"""





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

             # Test not batched input
             _lowerCamelCase				:  List[str]						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             _lowerCamelCase				:  Dict						=		True

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    with self.assertRaises(a__):
                           _lowerCamelCase				:  List[Any]						=		image_processor(
                               image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches

                    _lowerCamelCase				:  List[Any]						=		'''Hello'''

                    _lowerCamelCase				:  int						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__   ,				header_text=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Dict						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__   ,				header_text=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		self.image_processing_class(**self.image_processor_dict)
             # create random numpy tensors
             _lowerCamelCase				:  Tuple						=		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=a__   ,				numpify=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__   ,				np.ndarray)

             _lowerCamelCase				:  Any						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  List[str]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Tuple						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		self.image_processing_class(**self.image_processor_dict)
             # create random PyTorch tensors
             _lowerCamelCase				:  str						=		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
             _lowerCamelCase				:  Tuple						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  Optional[int]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  List[str]						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,)
@require_torch
@require_vision
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   PixaStructImageProcessor if is_vision_available() else None


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		PixaStructImageProcessingTester(self   ,				num_channels=4)
             _lowerCamelCase				:  Union[str, Any]						=		3


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self.image_processor_tester.prepare_image_processor_dict()


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  str						=		self.image_processing_class(**self.image_processor_dict)
             self.assertTrue(hasattr(a__   ,				'''do_normalize'''))
             self.assertTrue(hasattr(a__   ,				'''do_convert_rgb'''))



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		self.image_processing_class(**self.image_processor_dict)
             # create random PIL images
             _lowerCamelCase				:  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
             _lowerCamelCase				:  List[Any]						=		(
                 (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
                 * (self.image_processor_tester.num_channels - 1)
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                    # Test not batched input
                    _lowerCamelCase				:  Union[str, Any]						=		image_processor(
                        image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

                    # Test batched
                    _lowerCamelCase				:  Union[str, Any]						=		image_processor(
                        a__   ,				return_tensors='''pt'''   ,				max_patches=a__).flattened_patches
                    self.assertEqual(
                        encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)

