






"""simple docstring"""
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      UpperCamelCase_			:





				@staticmethod
				def 	_SCREAMING_SNAKE_CASE ( *lowerCAmelCase_   :					Dict      ,      **lowerCAmelCase_   :					Dict		)		->     Optional[int]:
							pass







@is_pipeline_test
@require_vision
@require_torch
class      UpperCamelCase_			(unittest.TestCase	):
	__magic_name__       =							MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING





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

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





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

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





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





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

				UpperCAmelCase_	:					Dict     	=   object_detector(
				    "./tests/fixtures/tests_samples/COCO/000000039769.png"      ,      candidate_labels=["cat", "remote", "couch"]      ,      threshold=0.6_4      ,      )

				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      decimals=4		)      ,      [
				        {"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
				        {"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
				        {"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
				        {"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
				        {"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
				        {"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
				        {"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
				        {"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
				        {"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
				    ]      ,      )

				UpperCAmelCase_	:					Dict     	=   object_detector(
				    [
				        {
				            "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
				            "candidate_labels": ["cat", "remote", "couch"],
				        }
				    ]      ,      threshold=0.6_4      ,      )

				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      decimals=4		)      ,      [
				        [
				            {"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
				            {"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
				            {"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
				            {"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
				            {"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
				            {"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
				            {"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
				            {"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
				            {"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
				        ]
				    ]      ,      )





	@require_torch
	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Tuple:
				UpperCAmelCase_	:					int     	=   pipeline("zero-shot-object-detection"		)

				UpperCAmelCase_	:					Any     	=   object_detector(
				    "http://images.cocodataset.org/val2017/000000039769.jpg"      ,      candidate_labels=["cat", "remote", "couch"]      ,      )
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      decimals=4		)      ,      [
				        {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
				        {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
				        {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
				        {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
				        {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
				    ]      ,      )

				UpperCAmelCase_	:					List[str]     	=   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(lowerCAmelCase_      ,      decimals=4		)      ,      [
				        [
				            {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
				            {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
				            {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
				            {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
				            {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
				        ],
				        [
				            {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
				            {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
				            {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
				            {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
				            {"score": 0.1_2_0_8, "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   :					List[str]		)		->     str:
				pass





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

				UpperCAmelCase_	:					Optional[int]     	=   object_detector(
				    "http://images.cocodataset.org/val2017/000000039769.jpg"      ,      candidate_labels=["cat", "remote", "couch"]      ,      threshold=lowerCAmelCase_      ,      )
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      decimals=4		)      ,      [
				        {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
				        {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
				        {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
				    ]      ,      )





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

				UpperCAmelCase_	:					Dict     	=   object_detector(
				    "http://images.cocodataset.org/val2017/000000039769.jpg"      ,      candidate_labels=["cat", "remote", "couch"]      ,      top_k=lowerCAmelCase_      ,      )
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      decimals=4		)      ,      [
				        {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
				        {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
				    ]      ,      )




'''simple docstring'''




# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def 							snake_case__				(     _A:   Dict					)       ->      str:

		'''simple docstring'''


		lowerCAmelCase     =    [False] * len(_A					)
		lowerCAmelCase     =    [-1] * len(_A					)

		def dfs(_A:   Dict ,  _A:   Tuple					):
				lowerCAmelCase     =    True
				lowerCAmelCase     =    c
				for u in graph[v]:
						if not visited[u]:
								dfs(_A ,  1 - c					)

		for i in range(len(_A					)					):
				if not visited[i]:
						dfs(_A ,  0					)

		for i in range(len(_A					)					):
				for j in graph[i]:
						if color[i] == color[j]:
								return False

		return True


# Adjacency list of graph
__lowercase        =    {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}




print(check_bipartite_dfs(graph))




