






import heapq
import sys

import numpy as np

A__			:    Tuple         =				tuple[int, int]
class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    []
								lowercase__     				=    set()




				def        lowercase__     (  self		:					List[str]						):

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




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								return len(self.elements						) == 0




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								if item not in self.set:
												heapq.heappush(self.elements,						(priority, item)						)
												self.set.add(lowerCamelCase						)
								else:
												# update
												# print("update", item)
												lowercase__     				=    []
												((lowercase__)				,		(lowercase__))     				=    heapq.heappop(self.elements						)
												while x != item:
																temp.append((pri, x)						)
																((lowercase__)				,		(lowercase__))     				=    heapq.heappop(self.elements						)
												temp.append((priority, item)						)
												for pro, xxx in temp:
																heapq.heappush(self.elements,						(pro, xxx)						)




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					List[Any]						):

								'''simple docstring'''
								if item in self.set:
												self.set.remove(lowerCamelCase						)
												lowercase__     				=    []
												((lowercase__)				,		(lowercase__))     				=    heapq.heappop(self.elements						)
												while x != item:
																temp.append((pro, x)						)
																((lowercase__)				,		(lowercase__))     				=    heapq.heappop(self.elements						)
												for prito, yyy in temp:
																heapq.heappush(self.elements,						(prito, yyy)						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								return self.elements[0][1]




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								((lowercase__)				,		(lowercase__))     				=    heapq.heappop(self.elements						)
								self.set.remove(lowerCamelCase						)
								return (priority, item)






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				# euclidean distance
				lowercase__     				=    np.array(lowerCamelCase_       )
				lowercase__     				=    np.array(lowerCamelCase_       )
				return np.linalg.norm(a - b       )






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				# integer division by time variable
				return consistent_heuristic(lowerCamelCase_	,     lowerCamelCase_       ) // t






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				# manhattan distance
				return abs(p[0] - goal[0]       ) + abs(p[1] - goal[1]       )






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    g_function[start] + Wa * heuristics[i](lowerCamelCase_	,     lowerCamelCase_       )
				return ans






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    np.chararray((n, n)       )
				for i in range(lowerCamelCase_       ):
								for j in range(lowerCamelCase_       ):
												lowercase__     				=    '''*'''

				for i in range(lowerCamelCase_       ):
								for j in range(lowerCamelCase_       ):
												if (j, (n - 1) - i) in blocks:
																lowercase__     				=    '''#'''

				lowercase__     				=    '''-'''
				lowercase__     				=    back_pointer[goal]
				while x != start:
								((lowercase__)				,		(lowercase__))     				=    x
								# print(x)
								lowercase__     				=    '''-'''
								lowercase__     				=    back_pointer[x]
				lowercase__     				=    '''-'''

				for i in range(lowerCamelCase_       ):
								for j in range(lowerCamelCase_       ):
												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:-'''       )
				lowercase__     				=    back_pointer[goal]
				while x != start:
								print(lowerCamelCase_	,     end=''' '''       )
								lowercase__     				=    back_pointer[x]
				print(lowerCamelCase_       )
				sys.exit()






def 						a    (		lowerCamelCase_       ):






				'''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 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     ):






				'''simple docstring'''
				for itera in range(lowerCamelCase_       ):
								open_list[itera].remove_element(lowerCamelCase_       )
				# print("s", s)
				# print("j", j)
				((lowercase__)				,		(lowercase__))     				=    s
				lowercase__     				=    (x - 1, y)
				lowercase__     				=    (x + 1, y)
				lowercase__     				=    (x, y + 1)
				lowercase__     				=    (x, y - 1)

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

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






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    []
				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__			:    Dict         =				{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

A__			:    Optional[int]         =				[
    (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__			:    Optional[Any]         =				make_common_ground()


A__			:    Optional[Any]         =				blocks_blk
# hyper parameters
A__			:    Tuple         =				1
A__			:    Optional[Any]         =				1
A__			:    List[Any]         =				20
A__			:    Optional[Any]         =				3  # one consistent and two other inconsistent

# start and end destination
A__			:    int         =				(0, 0)
A__			:    Tuple         =				(n - 1, n - 1)

A__			:    str         =				1






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    {start: 0, goal: float('''inf'''       )}
				lowercase__     				=    {start: -1, goal: -1}
				lowercase__     				=    []
				lowercase__     				=    set()

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

				lowercase__     				=    []
				lowercase__     				=    []
				while open_list[0].minkey() < float('''inf'''       ):
								for i in range(1	,     lowerCamelCase_       ):
												# 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(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       )
																else:
																				lowercase__				,		lowercase__     				=    open_list[i].top_show()
																				visited.add(lowerCamelCase_       )
																				expand_state(
																				    lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     )
																				close_list_inad.append(lowerCamelCase_       )
												else:
																if g_function[goal] <= open_list[0].minkey():
																				if g_function[goal] < float('''inf'''       ):
																								do_something(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       )
																else:
																				lowercase__     				=    open_list[0].top_show()
																				visited.add(lowerCamelCase_       )
																				expand_state(
																				    lowerCamelCase_	,     0	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     )
																				close_list_anchor.append(lowerCamelCase_       )
				print('''No path found to goal'''       )
				print()
				for i in range(n - 1	,     -1	,     -1       ):
								for j in range(lowerCamelCase_       ):
												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

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 MobileNetVaImageProcessor
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					Any,						lowerCamelCase		:					Tuple=7,						lowerCamelCase		:					str=3,						lowerCamelCase		:					Tuple=18,						lowerCamelCase		:					int=30,						lowerCamelCase		:					Tuple=400,						lowerCamelCase		:					Any=True,						lowerCamelCase		:					Any=None,						lowerCamelCase		:					List[str]=True,						lowerCamelCase		:					Union[str, Any]=None,						):

								'''simple docstring'''
								lowercase__     				=    size if size is not None else {'''shortest_edge''': 20}
								lowercase__     				=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    num_channels
								lowercase__     				=    image_size
								lowercase__     				=    min_resolution
								lowercase__     				=    max_resolution
								lowercase__     				=    do_resize
								lowercase__     				=    size
								lowercase__     				=    do_center_crop
								lowercase__     				=    crop_size




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}



@require_torch
@require_vision
class    _UpperCAmelCase      ( A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= MobileNetVaImageProcessor if is_vision_available() else None




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    MobileNetVaImageProcessingTester(self						)




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

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_resize'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''size'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_center_crop'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''crop_size'''						)						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class.from_dict(self.image_processor_dict						)
								self.assertEqual(image_processor.size,						{'''shortest_edge''': 20}						)
								self.assertEqual(image_processor.crop_size,						{'''height''': 18, '''width''': 18}						)

								lowercase__     				=    self.image_processing_class.from_dict(self.image_processor_dict,						size=42,						crop_size=84						)
								self.assertEqual(image_processor.size,						{'''shortest_edge''': 42}						)
								self.assertEqual(image_processor.crop_size,						{'''height''': 84, '''width''': 84}						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								lowercase__     				=    prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						Image.Image						)

								# Test not batched input
								lowercase__     				=    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__     				=    image_processing(lowerCamelCase,						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								lowercase__     				=    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
								lowercase__     				=    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__     				=    image_processing(lowerCamelCase,						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								lowercase__     				=    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
								lowercase__     				=    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__     				=    image_processing(lowerCamelCase,						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)




