



import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
	from PIL import Image
else:






	class 		lowercase						:






		"""simple docstring"""


		@staticmethod
		def   _SCREAMING_SNAKE_CASE		(     *__UpperCAmelCase     :						Any						,  **__UpperCAmelCase     :						str					)      ->			int:
								pass






@is_pipeline_test
@require_vision
@require_torch
class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	a__				: Union[str, Any]		       =							MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Optional[Any]						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						List[Any]					)      ->			List[str]:
							UpperCAmelCase_=       pipeline(
							    """zero-shot-object-detection"""						,  model="""hf-internal-testing/tiny-random-owlvit-object-detection"""					)

							UpperCAmelCase_=       [
							    {
							        """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
							        """candidate_labels""": ["""cat""", """remote""", """couch"""],
							    }
							]
							return object_detector, examples




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict						,  __UpperCAmelCase     :						List[Any]						,  __UpperCAmelCase     :						List[str]					)      ->			Optional[int]:
							UpperCAmelCase_=       object_detector(examples[0]						,  threshold=0.0					)

							UpperCAmelCase_=       len(__UpperCAmelCase					)
							self.assertGreater(__UpperCAmelCase						,  0					)
							self.assertEqual(
							    __UpperCAmelCase						,  [
							        {
							            """score""": ANY(__UpperCAmelCase					),
							            """label""": ANY(__UpperCAmelCase					),
							            """box""": {"""xmin""": ANY(__UpperCAmelCase					), """ymin""": ANY(__UpperCAmelCase					), """xmax""": ANY(__UpperCAmelCase					), """ymax""": ANY(__UpperCAmelCase					)},
							        }
							        for i in range(__UpperCAmelCase					)
							    ]						,  )




	@require_tf
	@unittest.skip("""Zero Shot Object Detection not implemented in TF"""					)
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			int:
							pass




	@require_torch
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			List[Any]:
							UpperCAmelCase_=       pipeline(
							    """zero-shot-object-detection"""						,  model="""hf-internal-testing/tiny-random-owlvit-object-detection"""					)

							UpperCAmelCase_=       object_detector(
							    """./tests/fixtures/tests_samples/COCO/000000039769.png"""						,  candidate_labels=["""cat""", """remote""", """couch"""]						,  threshold=0.64						,  )

							self.assertEqual(
							    nested_simplify(__UpperCAmelCase						,  decimals=4					)						,  [
							        {"""score""": 0.7_235, """label""": """cat""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
							        {"""score""": 0.7_218, """label""": """remote""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
							        {"""score""": 0.7_184, """label""": """couch""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
							        {"""score""": 0.6_748, """label""": """remote""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
							        {"""score""": 0.6_656, """label""": """cat""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
							        {"""score""": 0.6_614, """label""": """couch""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
							        {"""score""": 0.6_456, """label""": """remote""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
							        {"""score""": 0.642, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 274, """xmax""": 93, """ymax""": 297}},
							        {"""score""": 0.6_419, """label""": """cat""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
							    ]						,  )

							UpperCAmelCase_=       object_detector(
							    [
							        {
							            """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
							            """candidate_labels""": ["""cat""", """remote""", """couch"""],
							        }
							    ]						,  threshold=0.64						,  )

							self.assertEqual(
							    nested_simplify(__UpperCAmelCase						,  decimals=4					)						,  [
							        [
							            {"""score""": 0.7_235, """label""": """cat""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
							            {"""score""": 0.7_218, """label""": """remote""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
							            {"""score""": 0.7_184, """label""": """couch""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
							            {"""score""": 0.6_748, """label""": """remote""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
							            {"""score""": 0.6_656, """label""": """cat""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
							            {"""score""": 0.6_614, """label""": """couch""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
							            {"""score""": 0.6_456, """label""": """remote""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
							            {"""score""": 0.642, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 274, """xmax""": 93, """ymax""": 297}},
							            {"""score""": 0.6_419, """label""": """cat""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
							        ]
							    ]						,  )




	@require_torch
	@slow
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Any:
							UpperCAmelCase_=       pipeline("""zero-shot-object-detection"""					)

							UpperCAmelCase_=       object_detector(
							    """http://images.cocodataset.org/val2017/000000039769.jpg"""						,  candidate_labels=["""cat""", """remote""", """couch"""]						,  )
							self.assertEqual(
							    nested_simplify(__UpperCAmelCase						,  decimals=4					)						,  [
							        {"""score""": 0.2_868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
							        {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
							        {"""score""": 0.2_537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
							        {"""score""": 0.1_474, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
							        {"""score""": 0.1_208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
							    ]						,  )

							UpperCAmelCase_=       object_detector(
							    [
							        {
							            """image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
							            """candidate_labels""": ["""cat""", """remote""", """couch"""],
							        },
							        {
							            """image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
							            """candidate_labels""": ["""cat""", """remote""", """couch"""],
							        },
							    ]						,  )
							self.assertEqual(
							    nested_simplify(__UpperCAmelCase						,  decimals=4					)						,  [
							        [
							            {"""score""": 0.2_868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
							            {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
							            {"""score""": 0.2_537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
							            {"""score""": 0.1_474, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
							            {"""score""": 0.1_208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
							        ],
							        [
							            {"""score""": 0.2_868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
							            {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
							            {"""score""": 0.2_537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
							            {"""score""": 0.1_474, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
							            {"""score""": 0.1_208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
							        ],
							    ]						,  )




	@require_tf
	@unittest.skip("""Zero Shot Object Detection not implemented in TF"""					)
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[str]:
							pass




	@require_torch
	@slow
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Optional[Any]:
							UpperCAmelCase_=       0.2
							UpperCAmelCase_=       pipeline("""zero-shot-object-detection"""					)

							UpperCAmelCase_=       object_detector(
							    """http://images.cocodataset.org/val2017/000000039769.jpg"""						,  candidate_labels=["""cat""", """remote""", """couch"""]						,  threshold=__UpperCAmelCase						,  )
							self.assertEqual(
							    nested_simplify(__UpperCAmelCase						,  decimals=4					)						,  [
							        {"""score""": 0.2_868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
							        {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
							        {"""score""": 0.2_537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
							    ]						,  )




	@require_torch
	@slow
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			List[str]:
							UpperCAmelCase_=       2
							UpperCAmelCase_=       pipeline("""zero-shot-object-detection"""					)

							UpperCAmelCase_=       object_detector(
							    """http://images.cocodataset.org/val2017/000000039769.jpg"""						,  candidate_labels=["""cat""", """remote""", """couch"""]						,  top_k=__UpperCAmelCase						,  )
							self.assertEqual(
							    nested_simplify(__UpperCAmelCase						,  decimals=4					)						,  [
							        {"""score""": 0.2_868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
							        {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
							    ]						,  )









def 	__a					(   lowerCAmelCase_       : Dict    )				->			Dict:






						'''simple docstring'''



						return [
						    {
						        0: [1, 2],
						        1: [0, 2],
						        2: [0, 1, 3, 5],
						        3: [2, 4],
						        4: [3],
						        5: [2, 6, 8],
						        6: [5, 7],
						        7: [6, 8],
						        8: [5, 7],
						    },
						    {
						        0: [6],
						        1: [9],
						        2: [4, 5],
						        3: [4],
						        4: [2, 3],
						        5: [2],
						        6: [0, 7],
						        7: [6],
						        8: [],
						        9: [1],
						    },
						    {
						        0: [4],
						        1: [6],
						        2: [],
						        3: [5, 6, 7],
						        4: [0, 6],
						        5: [3, 8, 9],
						        6: [1, 3, 4, 7],
						        7: [3, 6, 8, 9],
						        8: [5, 7],
						        9: [5, 7],
						    },
						    {
						        0: [1, 3],
						        1: [0, 2, 4],
						        2: [1, 3, 4],
						        3: [0, 2, 4],
						        4: [1, 2, 3],
						    },
						][index]



def 	__a					(   lowerCAmelCase_       : dict[int, list[int]]    )				->			list[tuple[int, int]]:






						'''simple docstring'''



						UpperCAmelCase_=       0
						UpperCAmelCase_=       len(lowerCAmelCase_    )  # No of vertices in graph
						UpperCAmelCase_=       [0] * n
						UpperCAmelCase_=       [False] * n

						def dfs(lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : int    ):
												UpperCAmelCase_=       True
												UpperCAmelCase_=       id_
												id_ += 1
												for to in graph[at]:
																		if to == parent:
																								pass
																		elif not visited[to]:
																								dfs(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,id_    )
																								UpperCAmelCase_=       min(low[at]      ,low[to]    )
																								if id_ <= low[to]:
																														bridges.append((at, to) if at < to else (to, at)    )
																		else:
																								# This edge is a back edge and cannot be a bridge
																								UpperCAmelCase_=       min(low[at]      ,low[to]    )

						UpperCAmelCase_=       []
						for i in range(lowerCAmelCase_    ):
												if not visited[i]:
																		dfs(lowerCAmelCase_      ,-1      ,lowerCAmelCase_      ,id_    )
						return bridges


if __name__ == "__main__":
	import doctest

	doctest.testmod()




