
'''simple docstring'''

import heapq
import sys

import numpy as np

UpperCAmelCase__ :			Union[str, Any]											=		tuple[int, int]






class  A  :





	def __init__(       self      :							Optional[int]				):


				"""simple docstring"""

				lowerCAmelCase__										=				[]
				lowerCAmelCase__										=				set()





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				return len(self.elements				) == 0





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							Optional[int]   ,				__magic_name__      :							List[str]				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							Dict				):


				"""simple docstring"""

				if item in self.set:
							self.set.remove(__magic_name__				)
							lowerCAmelCase__										=				[]
							((lowerCAmelCase__)						,(lowerCAmelCase__))										=				heapq.heappop(self.elements				)
							while x != item:
										temp.append((pro, x)				)
										((lowerCAmelCase__)						,(lowerCAmelCase__))										=				heapq.heappop(self.elements				)
							for prito, yyy in temp:
										heapq.heappush(self.elements   ,				(prito, yyy)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				return self.elements[0][1]







	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				((lowerCAmelCase__)						,(lowerCAmelCase__))										=				heapq.heappop(self.elements				)
				self.set.remove(__magic_name__				)
				return (priority, item)







def 	A  (   UpperCamelCase_       :  TPos    ,						UpperCamelCase_       :  TPos   )       ->							Tuple:







			'''simple docstring'''



			lowerCAmelCase__										=				np.array(UpperCamelCase_   )
			lowerCAmelCase__										=				np.array(UpperCamelCase_   )
			return np.linalg.norm(a - b   )







def 	A  (   UpperCamelCase_       :  TPos    ,						UpperCamelCase_       :  TPos   )       ->							str:







			'''simple docstring'''



			return consistent_heuristic(UpperCamelCase_    ,						UpperCamelCase_   ) // t







def 	A  (   UpperCamelCase_       :  TPos    ,						UpperCamelCase_       :  TPos   )       ->							List[Any]:







			'''simple docstring'''



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







def 	A  (   UpperCamelCase_       :  TPos    ,						UpperCamelCase_       :  int    ,						UpperCamelCase_       :  TPos    ,						UpperCamelCase_       :  dict[TPos, float]   )       ->							Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__										=				g_function[start] + Wa * heuristics[i](UpperCamelCase_    ,						UpperCamelCase_   )
			return ans







def 	A  (   UpperCamelCase_       :  Dict    ,						UpperCamelCase_       :  Any    ,						UpperCamelCase_       :  List[str]   )       ->							Dict:







			'''simple docstring'''



			lowerCAmelCase__										=				np.chararray((n, n)   )
			for i in range(UpperCamelCase_   ):
						for j in range(UpperCamelCase_   ):
									lowerCAmelCase__										=				"*"

			for i in range(UpperCamelCase_   ):
						for j in range(UpperCamelCase_   ):
									if (j, (n - 1) - i) in blocks:
												lowerCAmelCase__										=				"#"

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

			for i in range(UpperCamelCase_   ):
						for j in range(UpperCamelCase_   ):
									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__										=				back_pointer[goal]
			while x != start:
						print(UpperCamelCase_    ,						end=" "   )
						lowerCAmelCase__										=				back_pointer[x]
			print(UpperCamelCase_   )
			sys.exit()







def 	A  (   UpperCamelCase_       :  TPos   )       ->							Union[str, Any]:







			'''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  (   UpperCamelCase_       :  List[str]    ,						UpperCamelCase_       :  Any    ,						UpperCamelCase_       :  List[str]    ,						UpperCamelCase_       :  Union[str, Any]    ,						UpperCamelCase_       :  List[Any]    ,						UpperCamelCase_       :  Dict    ,						UpperCamelCase_       :  Optional[Any]    ,						UpperCamelCase_       :  Any    ,						)       ->							int:







			'''simple docstring'''



			for itera in range(UpperCamelCase_   ):
						open_list[itera].remove_element(UpperCamelCase_   )
			# print("s", s)
			# print("j", j)
			((lowerCAmelCase__)						,(lowerCAmelCase__))										=				s
			lowerCAmelCase__										=				(x - 1, y)
			lowerCAmelCase__										=				(x + 1, y)
			lowerCAmelCase__										=				(x, y + 1)
			lowerCAmelCase__										=				(x, y - 1)

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

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







def 	A  (   )       ->							str:







			'''simple docstring'''



			lowerCAmelCase__										=				[]
			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


UpperCAmelCase__ :			Union[str, Any]											=		{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

UpperCAmelCase__ :			str											=		[
    (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),
]
UpperCAmelCase__ :			List[Any]											=		make_common_ground()


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

# start and end destination
UpperCAmelCase__ :			Optional[int]											=		(0, 0)
UpperCAmelCase__ :			Union[str, Any]											=		(n - 1, n - 1)

UpperCAmelCase__ :			Dict											=		1







def 	A  (   UpperCamelCase_       :  TPos    ,						UpperCamelCase_       :  TPos    ,						UpperCamelCase_       :  int   )       ->							Tuple:







			'''simple docstring'''



			lowerCAmelCase__										=				{start: 0, goal: float("inf"   )}
			lowerCAmelCase__										=				{start: -1, goal: -1}
			lowerCAmelCase__										=				[]
			lowerCAmelCase__										=				set()

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

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








"""simple docstring"""





import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:







							class 							SCREAMING_SNAKE_CASE   :






												"""simple docstring"""



												@staticmethod
												def 			__lowerCAmelCase					(			*lowercase_     :							Dict  ,**lowercase_     :							str						):
													pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			MODEL_FOR_OBJECT_DETECTION_MAPPING







					def 			__lowerCAmelCase					(			self     :							str  ,lowercase_     :							int  ,lowercase_     :							str  ,lowercase_     :							List[str]						):
						lowerCAmelCase__      :		int    							=      ObjectDetectionPipeline(model=lowercase_  ,image_processor=lowercase_						)
						return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







					def 			__lowerCAmelCase					(			self     :							int  ,lowercase_     :							str  ,lowercase_     :							str						):
						lowerCAmelCase__      :		List[str]    							=      object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  ,threshold=0.0						)

						self.assertGreater(len(lowercase_						)  ,0						)
						for detected_object in outputs:
							self.assertEqual(
							    lowercase_  ,{
							        '''score''': ANY(lowercase_						),
							        '''label''': ANY(lowercase_						),
							        '''box''': {'''xmin''': ANY(lowercase_						), '''ymin''': ANY(lowercase_						), '''xmax''': ANY(lowercase_						), '''ymax''': ANY(lowercase_						)},
							    }  ,)

						import datasets

						lowerCAmelCase__      :		str    							=      datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''  ,'''image'''  ,split='''test'''						)

						lowerCAmelCase__      :		Dict    							=      [
						    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						),
						    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						    # RGBA
						    dataset[0]['''file'''],
						    # LA
						    dataset[1]['''file'''],
						    # L
						    dataset[2]['''file'''],
						]
						lowerCAmelCase__      :		Optional[Any]    							=      object_detector(lowercase_  ,threshold=0.0						)

						self.assertEqual(len(lowercase_						)  ,len(lowercase_						)						)
						for outputs in batch_outputs:
							self.assertGreater(len(lowercase_						)  ,0						)
							for detected_object in outputs:
								self.assertEqual(
								    lowercase_  ,{
								        '''score''': ANY(lowercase_						),
								        '''label''': ANY(lowercase_						),
								        '''box''': {'''xmin''': ANY(lowercase_						), '''ymin''': ANY(lowercase_						), '''xmax''': ANY(lowercase_						), '''ymax''': ANY(lowercase_						)},
								    }  ,)







					@require_tf
					@unittest.skip('''Object detection not implemented in TF'''						)
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						pass







					@require_torch
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						lowerCAmelCase__      :		Optional[Any]    							=      '''hf-internal-testing/tiny-detr-mobilenetsv3'''

						lowerCAmelCase__      :		Union[str, Any]    							=      AutoModelForObjectDetection.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Any    							=      AutoFeatureExtractor.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Optional[int]    							=      ObjectDetectionPipeline(model=lowercase_  ,feature_extractor=lowercase_						)

						lowerCAmelCase__      :		Any    							=      object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''  ,threshold=0.0						)

						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						        {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						    ]  ,)

						lowerCAmelCase__      :		List[Any]    							=      object_detector(
						    [
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						    ]  ,threshold=0.0  ,)

						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        [
						            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						        ],
						        [
						            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						        ],
						    ]  ,)







					@require_torch
					@slow
					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		List[Any]    							=      '''facebook/detr-resnet-50'''

						lowerCAmelCase__      :		Any    							=      AutoModelForObjectDetection.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Dict    							=      AutoFeatureExtractor.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Any    							=      ObjectDetectionPipeline(model=lowercase_  ,feature_extractor=lowercase_						)

						lowerCAmelCase__      :		Tuple    							=      object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						    ]  ,)

						lowerCAmelCase__      :		List[Any]    							=      object_detector(
						    [
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						    ]						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        [
						            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						        [
						            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						    ]  ,)







					@require_torch
					@slow
					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		Tuple    							=      '''facebook/detr-resnet-50'''

						lowerCAmelCase__      :		Tuple    							=      pipeline('''object-detection'''  ,model=lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						    ]  ,)

						lowerCAmelCase__      :		Any    							=      object_detector(
						    [
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						    ]						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        [
						            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						        [
						            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						    ]  ,)







					@require_torch
					@slow
					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		List[Any]    							=      0.9985
						lowerCAmelCase__      :		Dict    							=      '''facebook/detr-resnet-50'''

						lowerCAmelCase__      :		List[Any]    							=      pipeline('''object-detection'''  ,model=lowercase_						)

						lowerCAmelCase__      :		int    							=      object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''  ,threshold=lowercase_						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						    ]  ,)







					@require_torch
					@require_pytesseract
					@slow
					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		Optional[Any]    							=      '''Narsil/layoutlmv3-finetuned-funsd'''
						lowerCAmelCase__      :		List[Any]    							=      0.9993

						lowerCAmelCase__      :		Any    							=      pipeline('''object-detection'''  ,model=lowercase_  ,threshold=lowercase_						)

						lowerCAmelCase__      :		List[str]    							=      object_detector(
						    '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_9_4, '''ymin''': 2_5_4, '''xmax''': 3_4_3, '''ymax''': 2_6_4}},
						        {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_9_4, '''ymin''': 2_5_4, '''xmax''': 3_4_3, '''ymax''': 2_6_4}},
						    ]  ,)




