



"""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 	snake_case ( *lowerCamelCase							:						Optional[Any],						**lowerCamelCase							:						Any  )->      List[Any]:
										pass

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


					'''simple docstring'''





					_a         =							MODEL_FOR_OBJECT_DETECTION_MAPPING



					def 	snake_case ( self							:						Tuple,						lowerCamelCase							:						Any,						lowerCamelCase							:						Dict,						lowerCamelCase							:						str  )->      str:
								lowerCamelCase__	:	Dict				=ObjectDetectionPipeline(model=lowerCamelCase,						image_processor=lowerCamelCase  )
								return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]



					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Tuple  )->      List[str]:
								lowerCamelCase__	:	List[Any]				=object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png''',						threshold=0.0  )

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

								import datasets

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

								lowerCamelCase__	:	Optional[Any]				=[
								    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__	:	Dict				=object_detector(lowerCamelCase,						threshold=0.0  )

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



					@require_tf
					@unittest.skip('''Object detection not implemented in TF'''  )
					def 	snake_case ( self							:						List[str]  )->      Dict:
								pass



					@require_torch
					def 	snake_case ( self							:						int  )->      Union[str, Any]:
								lowerCamelCase__	:	int				='''hf-internal-testing/tiny-detr-mobilenetsv3'''

								lowerCamelCase__	:	List[Any]				=AutoModelForObjectDetection.from_pretrained(lowerCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				=AutoFeatureExtractor.from_pretrained(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=ObjectDetectionPipeline(model=lowerCamelCase,						feature_extractor=lowerCamelCase  )

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

								self.assertEqual(
								    nested_simplify(lowerCamelCase,						decimals=4  ),						[
								        {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
								        {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
								    ],						)

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

								self.assertEqual(
								    nested_simplify(lowerCamelCase,						decimals=4  ),						[
								        [
								            {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
								            {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
								        ],
								        [
								            {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
								            {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
								        ],
								    ],						)



					@require_torch
					@slow
					def 	snake_case ( self							:						Dict  )->      Union[str, Any]:
								lowerCamelCase__	:	List[Any]				='''facebook/detr-resnet-50'''

								lowerCamelCase__	:	Dict				=AutoModelForObjectDetection.from_pretrained(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=AutoFeatureExtractor.from_pretrained(lowerCamelCase  )
								lowerCamelCase__	:	Any				=ObjectDetectionPipeline(model=lowerCamelCase,						feature_extractor=lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''  )
								self.assertEqual(
								    nested_simplify(lowerCamelCase,						decimals=4  ),						[
								        {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
								        {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
								        {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
								        {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
								        {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
								    ],						)

								lowerCamelCase__	:	Tuple				=object_detector(
								    [
								        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
								        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
								    ]  )
								self.assertEqual(
								    nested_simplify(lowerCamelCase,						decimals=4  ),						[
								        [
								            {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
								            {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
								            {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
								            {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
								            {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
								        ],
								        [
								            {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
								            {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
								            {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
								            {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
								            {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
								        ],
								    ],						)



					@require_torch
					@slow
					def 	snake_case ( self							:						Optional[Any]  )->      List[Any]:
								lowerCamelCase__	:	List[Any]				='''facebook/detr-resnet-50'''

								lowerCamelCase__	:	Union[str, Any]				=pipeline('''object-detection''',						model=lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''  )
								self.assertEqual(
								    nested_simplify(lowerCamelCase,						decimals=4  ),						[
								        {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
								        {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
								        {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
								        {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
								        {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
								    ],						)

								lowerCamelCase__	:	Dict				=object_detector(
								    [
								        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
								        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
								    ]  )
								self.assertEqual(
								    nested_simplify(lowerCamelCase,						decimals=4  ),						[
								        [
								            {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
								            {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
								            {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
								            {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
								            {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
								        ],
								        [
								            {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
								            {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
								            {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
								            {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
								            {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
								        ],
								    ],						)



					@require_torch
					@slow
					def 	snake_case ( self							:						Any  )->      List[Any]:
								lowerCamelCase__	:	Tuple				=0.9_985
								lowerCamelCase__	:	int				='''facebook/detr-resnet-50'''

								lowerCamelCase__	:	Optional[int]				=pipeline('''object-detection''',						model=lowerCamelCase  )

								lowerCamelCase__	:	Any				=object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''',						threshold=lowerCamelCase  )
								self.assertEqual(
								    nested_simplify(lowerCamelCase,						decimals=4  ),						[
								        {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
								        {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
								    ],						)




					@require_torch
					@require_pytesseract
					@slow
					def 	snake_case ( self							:						Union[str, Any]  )->      Union[str, Any]:
								lowerCamelCase__	:	Union[str, Any]				='''Narsil/layoutlmv3-finetuned-funsd'''
								lowerCamelCase__	:	Dict				=0.9_993

								lowerCamelCase__	:	List[str]				=pipeline('''object-detection''',						model=lowerCamelCase,						threshold=lowerCamelCase  )

								lowerCamelCase__	:	List[Any]				=object_detector(
								    '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''  )
								self.assertEqual(
								    nested_simplify(lowerCamelCase,						decimals=4  ),						[
								        {'''score''': 0.9_993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
								        {'''score''': 0.9_993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
								    ],						)









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					list[int]			):
			"""simple docstring"""

			# 1. Validate that path exists between current and next vertices
			if graph[path[curr_ind - 1]][next_ver] == 0:
						return False

			# 2. Validate that next vertex is not already in path
			return not any(vertex == next_ver for vertex in path			)






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					list[int]							,	__lowerCamelCase       :					int			):
			"""simple docstring"""

			# Base Case
			if curr_ind == len(__lowerCamelCase			):
						# return whether path exists between current and starting vertices
						return graph[path[curr_ind - 1]][path[0]] == 1

			# Recursive Step
			for next_ver in range(0							,	len(__lowerCamelCase			)			):
						if valid_connection(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			):
									# Insert current vertex  into path as next transition
									lowerCamelCase__	:	Tuple				=next_ver
									# Validate created path
									if util_hamilton_cycle(__lowerCamelCase							,	__lowerCamelCase							,	curr_ind + 1			):
												return True
									# Backtrack
									lowerCamelCase__	:	int				=-1
			return False






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					int = 0			):
			"""simple docstring"""
			lowerCamelCase__	:	Tuple				=[-1] * (len(__lowerCamelCase			) + 1)
			# initialize start and end of path with starting index
			lowerCamelCase__	:	Union[str, Any]				=start_index
			# evaluate and if we find answer return path either return empty array
			return path if util_hamilton_cycle(__lowerCamelCase							,	__lowerCamelCase							,	1			) else []




