


import heapq
import sys

import numpy as np

lowerCAmelCase							=						tuple[int, int]




class        lowerCamelCase      :


	def __init__(						self):
							__UpperCAmelCase :       Tuple  						=  []
							__UpperCAmelCase :       Union[str, Any]  						=  set()


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


	def    A(						self):
							return len(self.elements) == 0


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


	def    A(						self							,    lowercase__):
							if item in self.set:
													self.set.remove(lowercase__)
													__UpperCAmelCase :       List[Any]  						=  []
													((__UpperCAmelCase)						,			(__UpperCAmelCase)) :       Dict  						=  heapq.heappop(self.elements)
													while x != item:
																			temp.append((pro, x))
																			((__UpperCAmelCase)						,			(__UpperCAmelCase)) :       Optional[int]  						=  heapq.heappop(self.elements)
													for prito, yyy in temp:
																			heapq.heappush(self.elements							,    (prito, yyy))


	def    A(						self):
							return self.elements[0][1]


	def    A(						self):
							((__UpperCAmelCase)						,			(__UpperCAmelCase)) :       List[Any]  						=  heapq.heappop(self.elements)
							self.set.remove(lowercase__)
							return (priority, item)






def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    Optional[int]:




						'''simple docstring'''







						__UpperCAmelCase :       List[Any]  						=  np.array(lowercase_			)
						__UpperCAmelCase :       Optional[Any]  						=  np.array(lowercase_			)
						return np.linalg.norm(a - b			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    Optional[int]:




						'''simple docstring'''







						return consistent_heuristic(lowercase_			,					lowercase_			) // t




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    str:




						'''simple docstring'''







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




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)		->    Optional[int]:




						'''simple docstring'''







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




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			)		->    str:




						'''simple docstring'''







						__UpperCAmelCase :       Dict  						=  np.chararray((n, n)			)
						for i in range(lowercase_			):
												for j in range(lowercase_			):
																		__UpperCAmelCase :       str  						=  '''*'''

						for i in range(lowercase_			):
												for j in range(lowercase_			):
																		if (j, (n - 1) - i) in blocks:
																								__UpperCAmelCase :       List[str]  						=  '''#'''

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

						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:-'''			)
						__UpperCAmelCase :       Any  						=  back_pointer[goal]
						while x != start:
												print(lowercase_			,					end=''' '''			)
												__UpperCAmelCase :       Optional[Any]  						=  back_pointer[x]
						print(lowercase_			)
						sys.exit()




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    int:




						'''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 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					)		->    Union[str, Any]:




						'''simple docstring'''







						for itera in range(lowercase_			):
												open_list[itera].remove_element(lowercase_			)
						# print("s", s)
						# print("j", j)
						((__UpperCAmelCase)						,			(__UpperCAmelCase)) :       Optional[int]  						=  s
						__UpperCAmelCase :       Any  						=  (x - 1, y)
						__UpperCAmelCase :       Any  						=  (x + 1, y)
						__UpperCAmelCase :       List[Any]  						=  (x, y + 1)
						__UpperCAmelCase :       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_			)
																								__UpperCAmelCase :       Optional[int]  						=  -1
																								__UpperCAmelCase :       Dict  						=  float('''inf'''			)

																		if valid(lowercase_			) and g_function[neighbours] > g_function[s] + 1:
																								__UpperCAmelCase :       Tuple  						=  g_function[s] + 1
																								__UpperCAmelCase :       Tuple  						=  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 						__SCREAMING_SNAKE_CASE       (					)		->    List[Any]:




						'''simple docstring'''







						__UpperCAmelCase :       Optional[int]  						=  []
						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 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			)		->    Optional[int]:




						'''simple docstring'''







						__UpperCAmelCase :       List[Any]  						=  {start: 0, goal: float('''inf'''			)}
						__UpperCAmelCase :       Any  						=  {start: -1, goal: -1}
						__UpperCAmelCase :       List[str]  						=  []
						__UpperCAmelCase :       List[str]  						=  set()

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

						__UpperCAmelCase :       list[int]  						=  []
						__UpperCAmelCase :       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:
																														__UpperCAmelCase						,			__UpperCAmelCase :       Union[str, Any]  						=  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:
																														__UpperCAmelCase :       Tuple  						=  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 json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ImageGPTImageProcessor




class        lowerCamelCase      (     unittest.TestCase   ):


	def __init__(						self							,    lowercase__							,    lowercase__=7							,    lowercase__=3							,    lowercase__=1_8							,    lowercase__=3_0							,    lowercase__=4_0_0							,    lowercase__=True							,    lowercase__=None							,    lowercase__=True							,    ):
							__UpperCAmelCase :       Union[str, Any]  						=  size if size is not None else {'''height''': 1_8, '''width''': 1_8}
							__UpperCAmelCase :       Any  						=  parent
							__UpperCAmelCase :       Dict  						=  batch_size
							__UpperCAmelCase :       List[str]  						=  num_channels
							__UpperCAmelCase :       int  						=  image_size
							__UpperCAmelCase :       Tuple  						=  min_resolution
							__UpperCAmelCase :       str  						=  max_resolution
							__UpperCAmelCase :       Optional[int]  						=  do_resize
							__UpperCAmelCase :       Tuple  						=  size
							__UpperCAmelCase :       Union[str, Any]  						=  do_normalize


	def    A(						self):
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
							            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
							        ]),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}






@require_torch
@require_vision
class        lowerCamelCase      (     _UpperCamelCase       ,	unittest.TestCase   ):
	_lowerCAmelCase    :		Dict        =	ImageGPTImageProcessor if is_vision_available() else None


	def    A(						self):
							__UpperCAmelCase :       Optional[Any]  						=  ImageGPTImageProcessingTester(self)


	@property
	def    A(						self):
							return self.image_processor_tester.prepare_image_processor_dict()


	def    A(						self):
							__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class(**self.image_processor_dict)
							self.assertTrue(hasattr(lowercase__							,    '''clusters'''))
							self.assertTrue(hasattr(lowercase__							,    '''do_resize'''))
							self.assertTrue(hasattr(lowercase__							,    '''size'''))
							self.assertTrue(hasattr(lowercase__							,    '''do_normalize'''))


	def    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.image_processing_class.from_dict(self.image_processor_dict)
							self.assertEqual(image_processor.size							,    {'''height''': 1_8, '''width''': 1_8})

							__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class.from_dict(self.image_processor_dict							,    size=4_2)
							self.assertEqual(image_processor.size							,    {'''height''': 4_2, '''width''': 4_2})


	def    A(						self):
							__UpperCAmelCase :       Any  						=  self.image_processing_class(**self.image_processor_dict)
							__UpperCAmelCase :       Any  						=  json.loads(image_processor.to_json_string())
							for key, value in self.image_processor_dict.items():
													if key == "clusters":
																			self.assertTrue(np.array_equal(lowercase__							,    obj[key]))
													else:
																			self.assertEqual(obj[key]							,    lowercase__)


	def    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.image_processing_class(**self.image_processor_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase :       Dict  						=  os.path.join(lowercase__							,    '''image_processor.json''')
													image_processor_first.to_json_file(lowercase__)
													__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class.from_json_file(lowercase__).to_dict()

							__UpperCAmelCase :       Any  						=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
													if key == "clusters":
																			self.assertTrue(np.array_equal(lowercase__							,    image_processor_second[key]))
													else:
																			self.assertEqual(image_processor_first[key]							,    lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class(**self.image_processor_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													image_processor_first.save_pretrained(lowercase__)
													__UpperCAmelCase :       Dict  						=  self.image_processing_class.from_pretrained(lowercase__).to_dict()

							__UpperCAmelCase :       Optional[Any]  						=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
													if key == "clusters":
																			self.assertTrue(np.array_equal(lowercase__							,    image_processor_second[key]))
													else:
																			self.assertEqual(image_processor_first[key]							,    lowercase__)


	@unittest.skip('''ImageGPT requires clusters at initialization''')
	def    A(						self):
							pass






def 						__SCREAMING_SNAKE_CASE       (					)		->    int:




						'''simple docstring'''







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

						__UpperCAmelCase :       Optional[Any]  						=  Image.open(dataset[4]['''file''']			)
						__UpperCAmelCase :       Optional[int]  						=  Image.open(dataset[5]['''file''']			)

						__UpperCAmelCase :       int  						=  [imagea, imagea]

						return images





@require_vision
@require_torch
class        lowerCamelCase      (     unittest.TestCase   ):


	@slow
	def    A(						self):
							__UpperCAmelCase :       int  						=  ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small''')

							__UpperCAmelCase :       Any  						=  prepare_images()

							# test non-batched
							__UpperCAmelCase :       int  						=  image_processing(images[0]							,    return_tensors='''pt''')

							self.assertIsInstance(encoding.input_ids							,    torch.LongTensor)
							self.assertEqual(encoding.input_ids.shape							,    (1, 1_0_2_4))

							__UpperCAmelCase :       int  						=  [3_0_6, 1_9_1, 1_9_1]
							self.assertEqual(encoding.input_ids[0, :3].tolist()							,    lowercase__)

							# test batched
							__UpperCAmelCase :       int  						=  image_processing(lowercase__							,    return_tensors='''pt''')

							self.assertIsInstance(encoding.input_ids							,    torch.LongTensor)
							self.assertEqual(encoding.input_ids.shape							,    (2, 1_0_2_4))

							__UpperCAmelCase :       Any  						=  [3_0_3, 1_3, 1_3]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()							,    lowercase__)

